With the busy pace of modern life, an increasing number of people are afflicted by lifestyle diseases. Going directly to the hospital for medical checks is not only time-consuming but also costly. Fortunately, the emergence of rapid tests has alleviated this burden. Accurately interpreting test results is extremely important; misinterpreting the results of rapid tests could lead to delayed medical treatment. Given that URS-10 serve as a rapid test capable of detecting 10 distinct parameters in urine samples, the results of assessing these parameters can offer insights into the subject’s physiological condition. These parameters encompass aspects such as metabolism, renal function, diabetes, urinary tract disorders, hemolytic diseases, and acid–base balance, among others. Although the operational procedure is straightforward, the variegated color changes exhibited in the outcomes of individual parameters render it challenging for lay users to deduce causal factors solely from color variations. Moreover, potential misinterpretations could arise due to visual discrepancies. In this study, we successfully developed a cloud-based health checkup system that can be used in an indoor environment. The system is used by placing a URS-10 test strip on a colorimetric board developed for this study, then using a smartphone application to take images which are uploaded to a server for cloud computing. Finally, the interpretation results are stored in the cloud and sent back to the smartphone to be checked by the user. Furthermore, to confirm whether the color calibration technology can eliminate color differences between different cameras, and also whether the colorimetric board and the urine test strips can perform color comparisons correctly in different light intensity environments, indoor environments that could simulate a specific light intensity were established for testing purposes. When comparing the experimental results to real test strips, only two groups failed to reach an identification success rate of 100%, and in both of these cases the success rate reached 95%. The experimental results confirmed that the system developed in this study was able to eliminate color differences between camera devices and could be used without special technical requirements or training. With the development of science and technology, more and more people in the modern world of today are experiencing so-called “diseases of civilization” . HoweverIn this study, we used the image-capture function of smartphones to acquire images of rapid urine test strips and compare the colors obtained using a program, to assist ordinary people in interpreting their urine test results. These results were uploaded to the cloud to create a home cloud system.Urine analysis is often used by doctors when diagnosing diseases. Urine is basically composed of water and solutes. About 91–96% of urine is water , with soAlthough URS-10 are easy to operate and involve a very short period of time, ordinary individuals without specialist training or equipment may not always be able to compare and interpret the colors of test strips with the naked eye, leading to doubts about the accuracy and completeness of any interpretation. Users may misjudge their test results, causing delays in seeking medical attention. Fortunately, due to the popularization of smartphones in recent years, almost all people now possess such a device. Compared with the human eye, it is relatively easy for machine vision to interpret URS-10 test results. In the field of machine vision, color space refers to a way of describing the numerical value of color. It is also a different way of expressing color. For example, in the printing field, the color space is converted to the CMYK color space before printing takes place . Among cColor calibration is also called color restoration, and there are many ways to achieve the corrections desired when using this technique. The first color calibration card (ColorChecker) was issued by Macbeth in 1976, and it is now an indispensable form of technology in the photography, film, and television industries . Using CCloud computing is the model for all current computing methods. Its various applications enable many everyday tasks to be carried out more quickly and effectively, with closer relation to the specific needs of individual users. For example, in 2014, Durresi et al. proposed an architecture combining cloud computing and real-time advertising on smartphones . In addiAs the smartphone has become an important tool for human beings worldwide, many researchers have sought to develop detection systems based on smartphone technology in recent studies. The relevant studies include research conducted by Ra et al. in 2017 on urine analysis . This stThe approach of this study offers users a convenient and accurate method of URS-10 interpretation, mitigating the potential medical risks resulting from misinterpretation. Besides enhancing usability, this convenient service encourages individuals to pay closer attention to their health status, subsequently reducing both medical and societal costs.In the system used in this study, a smartphone was used to capture an image of a rapid test strip and then send the captured image to a server. The server then interpreted the test results from the image and created a user-specific folder to store both the captured image and the test results. Finally, the results of the analysis were sent back to the smartphone application and made available to the user, who could read the records in the future. The system flow is shown in This system consisted of four parts: server; mobile application; hardware; and image-processing program.The server was managed by XAMPP, which is a free and open-source cross-platform network server management tool. In the current research, the server employed for computing the testing results was a computer host with the LINUX operating system, on which XAMPP had been installed. The functionalities of XAMPP were harnessed to establish a cloud server and a database infrastructure. The system operated by generating individualized folders within the database, delineated by user-specific IDs. These folders were designated for users to upload captured photos. Subsequently, the server undertook the task of subjecting the uploaded photos to image processing procedures.The mobile application was developed for use on both the Android and iOS systems, and it enabled users to take pictures with the built-in camera in their smartphones. The interfaces of the mobile application are shown in ® 2018 and a colorimetric board above the platform designed in Adobe® Illustrator CC 2018 with a test strip placement site. Because pictures taken by users may have problems such as skew, offset, uneven sources of ambient light, motion blur, etc., the image recognition program in this study used various corrective methods, including perspective transformation, color calibration blocks, and positioning blocks, to overcome such problems. The colorimetric board designed for this study is shown in The hardware consisted of a colorimetric board unit, with a platform designed in AutoCADThe image-processing part of the studied system served four important functions: perspective transformation; block positioning; color calibration; and color space comparison. Perspective transformation is a technique of projecting an image onto a new visual plane. It is also known as projection mapping. Perspective transformation is widely used in 3D image processing. In 2013, Do proposed a method to apply perspective transformation to process image scaling . A skeweWe used the outer black positioning color blocks as a coordinate reference to calculate the coordinates of the calibration color blocks. Because black and white values in images are very different when converted to grayscale, we regarded the place where the grayscale value changed drastically as the edge of the positioning color block, and then found the coordinates and range of the positioning blocks and calculated the positions of all the calibration color blocks. Because test strips are placed by users, the detection color blocks on the test strips may not necessarily align with the black positioning blocks; however, the same method can still be used to find the color block. In grayscale images, the position of the test strip can be found by looking for the sharp change point perpendicular to the positioning block at the top-center of the colorimetric board.Xij is the value of each channel of color block, m is the length of the color block, and n is the width of the color block. The calculated average values of the RGB channels were used as RGB values for subsequent calculations of the correction color blocks. Subsequently, 24 RGB sample data were extracted from the prestored standard sample data and expressed using the following method:S represents the aggregate of RGB values from standard samples. The values extracted from the image for the correction color block after color correction should match those of the standard samples. The average value of the RGB pixels of each color block was first transformed into a polynomial coefficient matrix P [The color calibration method involved extracting pixels from the respective RGB channels of the correction color blocks on the colorimetric board from the image after perspective transformation  channel value of the color being tested and S1 represents the saturation channel value of the color being tested. V2 and S2 are the values of the standard sample. dH represents the difference in the hue channel between the color being tested and the standard sample. If H1 minus H2 is less than 180, dH can be represented as follows:The respective distances between the sample color and points D1, D2, and D3 could then be compared. The lesser the distance, the more similar the color. The detailed calculation method is shown in the following equation :(7)DistadH can be represented as follows:Otherwise, The URS-10 test strips used in this study are a commercially available product, and the color charts provided by manufacturers may be used by inspection personnel to carry out naked-eye comparisons. The color charts were printed using general commodities, and the accuracy of the final printed color was affected by many variables, so there was a slight difference between the color of the test strip after the reaction and the printed color. Directly using the color charts supplied with commercially available products for the purpose of machine-vision recognition may have led to misjudgment, so a color chart was first used as a reference. The actual color could then be imported into the comparison chart for adjustment, so that it could be applied to image recognition, providing an accurate interpretation. As an illustration of this process, Because of this, the colors of the real test strips were used to correct HSV samples and thus improve accuracy in the interpretation of results. The correction process is shown in After the correction process, the HSV values of the test strips were retrieved and compared to those of HSV samples. If the comparison accuracy was lower than 95%, the HSV samples of the color chart were corrected with the test strip until the accuracy level exceeded 95%.In order to verify the accuracy of the interpretation of actual test strips by our proposed system, the experiment was carried out in an indoor natural light environment by dropping urine sample specimens onto the test strips. Test sample specimens were prepared by proportionally mixing the standard urine samples Liquichek™ Urinalysis Control Level 1 and Liquichek™ Urinalysis Control Level 2 (hereafter referred to as Level 1 and Level 2). Level 1 is negative, and Level 2 is strongly positive. In this experiment, serial dilution  was usedWe used four different smartphones in our experiments, namely, an iPhone 7 Plus, iPhone 12 PRO, Samsung Galaxy S10, and Redmi Note 8 Pro. Each smartphone was used 10 times for each item paired with each concentration. Due to the large amount of data generated, the data were integrated and presented as shown in following sections. Because of the limited amount of the standard sample itself, we were unable to use all possible concentrations of samples to test the test strips. Therefore, in the experiment involving actual test samples with different concentrations, each sample concentration was used only once per test strip.Experimental results prior to color correction are shown in The results in In order to verify the new HSV samples, two retests were carried out. The experimental method of the retest was the same as the experimental method described in the previous section. The experimental results show accuracy levels of at least 95% for each item, as shown in In the first retest, except for the pH item for samples with 12.5% concentration and the protein item for samples with 100% concentration, the accuracy rate for all items at all other concentrations was 100%. In the second retest, except for the comparison of the nitrite item for samples with 12.5% concentration, the specific gravity item for samples with 25% concentration, and the urobilinogen item for samples with 100% concentration, the accuracy rate for all items was again 100%.The experimental results therefore reveal that comparison accuracy was improved after the color correction process.Uneven light sources can be likened to introducing localized illumination during photography, causing changes in the RGB values of certain areas within the image. Such uneven light sources may be misconstrued as errors introduced by the imaging system during color correction. The ColorChecker technique employed in this study involves comparing the RGB values captured in the image with standard values and applying corrective measures. This process encompasses overall color correction of the image rather than addressing specific local regions. If the light source is uneven, it might lead to misinterpretations during conversion, resulting in an overall color distortion within the image. Example environments tested in this study, such as outdoor scenes and stairwell corners, fall within scenarios with highly uneven lighting conditions. To confirm whether our system was able to identify test strips photographed in different environments, actual test strips were used to conduct experiments in six environments with different illumination levels. The environmental data are shown in The experimental method was the same as in the previous experiment, and involved four different smartphones, each of which was used ten times for each item at all levels of concentration. The purpose of the experiment was mainly to explore the conditions under which recognition could correctly be achieved in various open environments. Correct numbers for all items in the same environment were counted together to obtain the accuracy rate for that environment. The experimental results are shown in It can be seen in these results that accuracy rates for the indoor work area and the indoor office area were relatively close. Accuracy rates for the corner of the stairs, the indoor kitchen, and the arcade were also similar. The outdoor accuracy rate was the lowest because the illumination was too high. From these results, it may be deduced that our system is sensitive to ambient light sources. Our proposed method is therefore unsuitable for outdoor environments with excessive brightness but it is suitable for indoor environments with sufficient general lighting.During the development process, numerous challenges were encountered. One of these challenges pertained to the discrepancy between the actual rapid test colors and the printed sample colors, as described earlier. Recognizing that variations in rapid test colors might also exist outside of the laboratory environment, multiple iterations were attempted before opting to employ actual rapid tests for calibration and adjustment alongside the printed sample colors.In addition to expanding the range of items under detection, future enhancements could be directed towards the image-processing segment. This might involve refining algorithms to mitigate the impact of uneven light sources, optimizing methods for image localization, and similar endeavors.In this study, we successfully developed a cloud detection system applied to the URS-10 urine test. By combining software and hardware, the system was able to achieve accurate results in indoor environments. Because it was executed in the cloud, errors in image processing errors and additional delays caused by differences in devices could be reduced.The image-processing function includes image-processing methods such as perspective transformation, color calibration, and color comparison. Through the use of these processes, the comparison accuracy of the system was improved, and factors that negatively affect detection results were eliminated as much as possible, so that the color comparison of test strips could be carried out smoothly. To confirm the performance of our system, real test strips with different sample concentrations were used for comparison purposes in a natural light environment. Initial comparison results revealed some lack of accuracy in the case of real test strips; indeed, none of the test results for the 16 groups indicated 100% accuracy, and only in five instances did accuracy reach 95%. However, after the HSV sample correction process, only two items did not reach 100% accuracy, and even these reached accuracy levels of 95%. The experimental results therefore indicate that the system was able to correctly identify test results in an indoor environment.Even in extreme brightness ranges of 5 lux and 1464 lux, the interpretation accuracy rate was still at least 80%. Furthermore, the method proposed in this study was able to eliminate identification errors caused by a tilted placement position. When taking pictures, it was only necessary to fully capture the colorimetric board and the URS-10 test strip in the frame; alignment to the center of the frame was not required. In addition, differences between camera brands and lenses were able to be eliminated after color calibration. The method proposed in this study can be directly tested with the commercially available URS-10 product, and the system has high scalability. In the future, new detection algorithms can be added to import other rapid-test products for detection.


Introduction: Our objective was to analyze and compare systematically and structurally reimbursement systems in Poland and other countries.Methods: The systems were selected based on recommendations issued by the Polish Agency for Health Technology Assessment and Tariffication (AHTAPol), which explicitly referred to other countries and agencies). Consequently, apart from Poland, the countries included in the analysis were England, Scotland, Wales, Ireland, France, Netherlands, Germany, Norway, Sweden, Canada, Australia and New Zealand. Relevant information and data were collected through a systematic search of PubMed (Medline), Embase and The Cochrane Library as well as competent authority websites and grey literature sources.Results and discussion: In most of the countries, the submission of a reimbursement application is initiated by a pharmaceutical company, and only a few countries allow it before a product is approved for marketing. All of the agencies analyzed are independent and some have regulatory function of reimbursement decision making body. A key criterion differentiating the various agencies in terms of HTA is the cost-effectiveness threshold. Most of the countries have specific mechanisms to improve access to expensive specialty drugs, including cancer drugs and those used for rare diseases. Reimbursement systems often lack consistency in appreciating the same stages, leading to heterogeneous decision-making processes. The analysis of recommendations issued in different countries for the same medicinal product will allow a better understanding of the relations between the reimbursement system, HTA assessment, stakeholders involvement and decision on reimbursement of innovative drugs. Health care systems around the world are currently facing similar challenges of ageing populations and increased demand for medical services, intensive medical development, more and more expensive medical technologies, and an increase in public expectations of treatment options and benefits . The demCountries examining the same clinical and economic evidence may make different coverage decisions . Poland In the case of a drug that has no equivalent (a new substance or a new indication), both the submission of an application and its evaluation by the AHTAPol  are required. AHTAPol is an independent organizational entity that collects data, conducts analyzes and issues reimbursement recommendations regarding the legitimacy of financing medicinal products, medical devices, foodstuffs for particular nutritional uses and health services from public funds. AHTAPol is also an advisory body for the Minister of Health—the collected analytical data and recommendations of AHTAPol support the Minister of Health in making reimbursement decisions. In the next stage, the complete file is submitted to the Economic Commission, which conducts negotiations with the applicant to determine the official selling price, the level of payment, and the indications on which the drug is to be reimbursed. Only with the recommendation of the President of the AHTAPol and with consideration to the position of the Economic Commission, taking into account the criteria listed in Article 12 of the aforementioned Act, the Minister of Health makes the final decision on a given drug for the requested indication .The main purpose of the paper is to analyze and compare in systematical and structural way reimbursement systems in Poland and other countries.Systems covered by the comparison were selected based on recommendations issued by the AHTAPol, which explicitly referred to other countries and agencies. Consequently, apart from Poland the countries included in the analysis were England, Scotland, Wales, Ireland, France, Netherlands, Germany, Norway, Sweden, Canada, Australia and New Zealand. The selection of the HTA agencies included resulted from Health Technology Assessment and Tariff Assessment Agency practice and explicit quoting in its recommendations. AHTAPol does not publish a formal list of reference agencies, thus we decided to identify them based on practice and published recommendations.The bibliographic databases searched include PubMed (Medline), Embase and The Cochrane Library. The strategy for searching the databases is shown in Data from the articles were extracted using a pre-designed analytical framework, the main purpose of which was to collect data on the general characteristics of the reimbursement systems in the countries analyzed.A comparison of the reimbursement systems in the countries analyzed is presented below and in In Poland and most of the countries analyzed, the availability of the product on the market is a condition for applying for reimbursement. However, it is worth noting that in some countries , an application for reimbursement can be submitted prior to marketing authorization, i.e., during the authorization procedure. Alike in England for some cancer drugs .In Canada, in the case of the Common Drug Review (CDR) program, the application covers all provinces except Quebec, where the process is conducted by INESSS , and can be initiated by the sponsor—both before and after the issuance of the NOC (permit). In the case of pan-Canadian Oncology Drug Review (pCODR), the application can also be initiated either before or after the NOC (Notice of Compliance) is issued. Application to INESSS begins with the manufacturer’s application for registration and can be initiated at any time  .In Poland the reimbursement process begins with the submission of the application and relevant files to the Ministry of Health as well as processing fee payment by the market authorization holder. When applying for reimbursement for a drug that has no equivalent in a given medical indication on the reimbursement list, the Minister of Health forwards the reimbursement dossier  to the Agency for Health Technology Assessment and Tarification (AHTAPol) .In England, Ireland and Wales, unlike most countries, the initiator of the reimbursement process is primarily the government. In Scotland the reimbursement process begins at the request of the marketing authorisation holder (MAH) or patients. Among the countries analyzed, the application for drug reimbursement can be submitted by various parties in healthcare - not only the responsible party, but also patients and clinicians. In majority of other countries, only pharmaceutical companies apply for reimbursement for medicinal products .In some countries, the reimbursement dossier is submitted to the Ministry of Health in Poland, Ireland and Netherlands, while in others, the dossier goes straight to the agency responsible for HTA  .In the countries analyzed, HTA agencies function mainly in the form of autonomous government bodies. The duties of HTA agencies can be carried out either by regulatory bodies responsible for reimbursement decisions, or by advisory bodies that provide their reimbursement recommendations to decision-makers  .The regulatory function is executed by agencies such as G-BA , TLV , NoMA , PHARMAC , NICE  and SMC . If the recommendation is negative, price negotiations can take place where the MAH can further demonstrate the cost-effectiveness of the therapy by lowering the price . In someIn countries where agencies have an advisory role, positive reimbursement recommendations do not always translate into final reimbursement coverage of a technology; also their impact on the outcome of price negotiations is not clear . The recommendation of the President of AHTAPol is also not conclusive, as the decision of the Minister of Health is mainly influenced by arrangements occurring at later stages of the process, such as a reduction in the drug price by MAH during negotiations. In Poland the outcome of the AHTAPol assessment may have a significant impact on the price negotiations .Different systems have adopted different organizational arrangements of reimbursement process, and the HTA agencies organization may also very. Most countries have a “light” model, under which agencies assess the quality of the analyses  . Other cThe countries analyzed can be categorized based on whether they consider as part of their HTA evaluation only clinical effectiveness and safety or both clinical benefit and cost-effectiveness, including the impact on the payer’s budget for health services .Poland, England, Scotland, Wales, Netherlands, Germany, Norway, Sweden, Canada, Australia and New Zealand, consider both the clinical and economic aspects, including impact on the payer’s budget for health services. In addition, in some countries additional aspects are evaluated, such as unmet medical need, the degree of innovation of the therapy being evaluated, and ethical, social, legal and organizational aspects .In most countries single technology are usually evaluated. England and Norway have three formats for developing the report. In England, HTA can be conducted as part of a multiple technology assessment—MTA , as part of a single technology assessment—STA (technology assessment of one drug or therapy in one indication), or as part of fast technology assessment - FTA (this is used to provide patients with faster access to the most cost-effective new treatments). In Norway, in addition to STA and MTA, a so-called mini HTA is used—a simplified assessment performed by clinical experts for in-hospital technologies .All countries recognize a variety of data sources, including scientific studies , national statistics, clinical guidelines, surveys, expert reports and other evidence from pharmaceutical manufacturers ..While some countries use costs to assess the value of a drug, others, such as France, evaluate the relative clinical benefit of the drug compared to existing comparators and use evidence from randomized clinical trials. The relative benefit of a drug is assessed on a 5-point scale based on the level of improvement due to clinical benefit , which is used to determine the reimbursement level . HTA ageNegotiations play a crucial role in reimbursement decisions in most of the countries analyzed, for both original and generic drugs.inter alia setting the official selling price in Poland, clinical indication under which the drug is to be reimbursed, and managed entry agreements (MEA)  . Accordits (MEA) . The fints (MEA) .In Netherlands, as in Poland, price negotiations with a pharmaceutical company influence the type of decision the Ministry of Health, Welfare and Sports makes. According to the Drug Pricing Law, the ministry sets maximum drug prices, which are subject to adjustment every 6 months—the reference point being drug prices in reference countries  .In Norway, The Norwegian Drug Procurement Cooperation (LIS) is responsible for negotiating prices for reimbursed drugs. If a positive reimbursement decision involves an increase in reimbursement expenditures of 10 million Euro over 5 years, then the NoMa is not authorized to grant reimbursement. In this case, as long as the application meets all the prioritization criteria, NoMA forwards the assessment to the Ministry of Health and Welfare .In Sweden, the institution responsible for reimbursement decisions and pricing is the Dental and Pharmaceutical Benefits Agency (TLV). Decisions on reimbursement and pricing of new drugs are made by an expert committee within the agency, i.e., The Pharmaceutical Benefits Board, which is appointed by the government and consists of seven members recruited from district councils, universities or patient organizations. The agency’s decisions are binding at both the national and local levels (district councils). The degree and implementation rate may vary from district to district due to individual budgeting mechanisms or different interpretations of the Agency’s recommendations .In some countries negotiations are conducted only if cost-effectiveness is not demonstrated in the HTA. In Ireland, if a drug is cost-effective, the Health Service Executive (HSE) may approve the drug for reimbursement. Otherwise, the drug is referred for evaluation by the HSE Drugs Group. At this stage, it is possible to conduct commercial negotiations with the applicant to work out mutually acceptable financing terms, or to enter into patient access schemes, which are reimbursement individual decisions .In England, any product that NICE has assessed as cost-effective, but may cost the NHS more than 20 million GBP in any of its first 3 years of use, must be subject to further negotiation between the manufacturer and the NHS. If negotiations fail, the NHS can ask NICE to delay funding for the product for up to 3 years . The Secretary of State for Health and Welfare is responsible for negotiating prices with pharmaceutical companies under the Pharmaceutical Price Regulation Scheme (PPRS), which is a voluntary agreement between the Department of Health and the Association of the British Pharmaceutical Industry (ABPI) for the supply of licensed, brand-name drugs to the NHS . In ScotIn other countries  the outcome of the clinical assessment is a key factor in price negotiations. In Germany if additional benefits are proven, within 6 months the GKV-SV  and the pharmaceutical company negotiate the reimbursement price . In FranIn Canada, after CADTH (or INESSS - in the case of Quebec) issues a positive recommendation, the pan-Canadian Pharmaceutical Alliance (pCPA) starts negotiations. In the first instance, the provinces decide whether negotiations will take place. They can either forgo negotiating a price or decide to negotiate collectively or individually. Provinces can also negotiate for the CADTH’s non-evaluated drug. If a decision is made to negotiate, a jurisdiction is chosen to represent all provinces in the negotiations. Negotiations are conducted with the participation of the provincial representative and the manufacturer and may include other terms in addition to the price . If an aIn Australia, although the final decision is made by the Minister of Health, it cannot be other than the PBAC’s recommendation. In cases where a decision to place a drug on the PBS list would result in a net cost exceeding USD 20 mln per year, the decision is made by the Federal Cabinet prior to the Minister’s decision .,68.In New Zealand, PHARMAC negotiates prices for inpatient, outpatient and cancer treatment, as well as prices for pharmaceuticals, vaccines and medical devices, and manages the limited state budget for outpatient and cancer drugs67Some countries have established mechanisms to improve access to highly specialized drugs, including those used to treat cancer and rare diseases .Poland has what is known as rescue access to drug technology (RADT), a system for issuing individual approvals for the treatment of patients for whom all publicly funded therapeutic options have been exhausted. Medicines financed under RADT are free of charge to patients, and their cost are covered by the NHF. Only the healthcare center directly involved in patient treatment can submit an application for RADT funding. Funding may cover drugs whose appropriateness for use in a particular case has been confirmed by a national or provincial consultant. However, the decision is issued for a specified period of time, after which the effectiveness of the treatment and the legitimacy of its continuation are assessed. An alternative path to access to drugs with high clinical innovation is financing under the Medical Fund .In England, the HST process is available for products that are dedicated to clinically distinct target patient group, which treatment is concentrated in a small number of NHS centers, the patient’s condition is chronic and severely disabling, and the technology has the potential for lifelong use. For these products, conventional NICE assessment assumes some allowance to account for the likely higher costs and often more limited clinical data. NICE typically recommends HTS which ICER is less than 100,000 GBP. In certain circumstances, based on clinical benefit magnitude, NICE can recommend products above this threshold, usually up to 300,000 GBP . EnglandIn Scotland, the SMC uses different evaluation criteria  and places less emphasis on the cost-effectiveness of cancer and rare diseases drugs. More weight is given to effects that are not necessarily quantified . If the drug meets the standard ICER criteria, the SMC may temporarily approve it. If not, the Peer and Clinician Engagement (PACE) process is initiated, which is not used in the standard approach. The PACE process relies on a consultative approach between patients and clinicians, who, identify additional effects that are relevant from the perspective of the patients, but difficult to quantify with ICER. This approach makes it possible to allow more expensive drugs to be used in the NHS. In the event of a negative SMC recommendation for a drug, a patient can access it using either tier 1 or tier 2 PACS, with tier 1 covering ultra-rare diseases and tier 2 covering all drugs that have not been approved by the SMC .In France, as an exception, innovative or very expensive drugs may be financed under an additional “reimbursement tariff” determined in advance by specific agreement with CEPS  .As in England and Scotland, higher values for cost-effectiveness are accepted in Canada for rare diseases and cancer drugs. For this purpose, a special team was set up to evaluate and make relevant recommendations, the pCODR, which replaced the Joint Oncology Drug Review (JODR) as of 2010. The evaluation process conducted by pCODR is formalized and time-framed, and takes into account the opinions of clinicians, economic experts, manufacturers and patients. The process of evaluating cancer drugs is similar to that used in treatments for other diseases and is based on analogous premises and data .In Germany, Australia and New Zealand, end-of-life medical technologies are not evaluated under a different procedure. In Germany, the legislator specifies that the additional medical benefit has already been proven in the registration process. Therefore, the assessment for the additional benefit categories are omitted for orphan drugs. Furthermore, for orphan drugs, an additional benefit does not have to be proven by comparison to an appropriate comparative therapy that had been previously approved by the G-BA77.Australia has another way of funding very expensive and cost-ineffective treatments for rare diseases, so called The Life Saving Drugs Program. It applies only to treatments which reduce mortality and prolong patient survival. There are no additional restrictions on the diseases or treatments that can be covered by the program. Reimbursement is on a patient-by-patient basis and depends on 10 criteria  .In New Zealand, the Rare Disorders Advisory Committee may reconsider the funding application if new evidence is provided .In Wales, Ireland and Norway there are currently no distinct financed pathway for oncological and orphan drugs .The regulatory time limit for the reimbursement process varies among countries. In majority of the countries the total duration of the process, from submission of an application to issuance of a reimbursement decision, should take no longer than 180 days. Norway and Canada have adopted a time limit of 90 days (for non-cancer drugs), while the process takes the longest in England, up to 305 days. However, it should be noted that in practice, decisions are rarely made within the timeframe specified in the regulations. Independently conducted analyses indicated the time to drug reimbursement is the longest in Poland (844 days), followed by New Zealand (789 days), Canada (602 days), Ireland (541 days), France (497 days), Australia (467 days), Scotland (417 days), Norway (414 days), England (340 days), Netherlands (294 days), Sweden (261) and Germany (133 days) .Considering the aspects analyzed within each country, the following reasons for the limited availability of innovative therapies can be identified.Although in some countries  it is possible to apply for reimbursement while still in the registration process this does not always translate into faster access to new drugs. An example of such a country is Canada. The reasons for the long delays in the introduction of new drugs on the market include the multi-layered sequential review process conducted before public drug plans make new innovative therapies available .In countries where reimbursement recommendations published by individual HTA agencies are binding, i.e., a positive recommendation equals reimbursement coverage , the time from registration to reimbursement is much shorter. The exception is New Zealand, where despite the regulatory function of the HTA agency, the actual average duration of the reimbursement process is time consuming. Finally, the implementation of the HTA recommendations can also vary, depending on the advisory or regulatory role of the HTA agency. In the case of Sweden, where the national HTA agency is a regulatory body which decisions are adopted by local administrative councils, not all councils can afford all reimbursement decisions, which can lead to inter-regional differences.The countries analyzed also have different approaches to in hospital drugs, generic and innovative drugs, the length of time for which a reimbursement decision is issued, and how drug expenses are accounted for and whether drugs should be replaced with cheaper substitutes. Finally negotiations may contribute substantially to longer time to reimbursement decision.The presence of specific mechanisms or more lenient criteria in the evaluation of clinical and economic evidence for cancer and rare disease drugs is also an important factor affecting access to breakthrough therapies. Poland is an example of a country where less restrictive HTA evaluation criteria do not apply. Poland has no specific reimbursement arrangements on drugs in rare diseases, which could be perceived as systematic approach to facilitate accurate and timely patients access. Medical Fund regulation only partially addresses that challenge for highly innovative products only.This paper addresses the similarities and differences of the reimbursement systems (with particular emphasis on HTA assessment) in Poland and 12 countries, which HTA bodies’ recommendations are most often quoted by the AHTAPol. In other similar studies usually either a smaller number of countries or a narrower range of aspects were covered . IndividIn most of the countries that have been studied, the process of submitting a reimbursement application is initiated by the pharmaceutical company. Only a few countries allow submission before a product is approved for marketing. The next steps in the reimbursement process are similar across most countries. After a formal and legal evaluation of the application, it is sent for assessment by the HTA agency/authority. In several countries the assessment, appraisal, and subsequent final recommendation have a binding effect on the drug’s coverage from the public budget. Price negotiations take place in each country, although in some cases, it is not mandatory, such as when ICER  is below the threshold. Regarding subsequent stages of the reimbursement process, such as the reimbursement decision and the institution responsible for financing the reimbursed drug, there are significant differences from country to country. Although the reimbursement systems share the same goal, the way to final decision and HTA use to support it can be organized much differently, thus one universal model cannot be claimed, and studies of existing arrangements are justified.More similarities can be seen in the functioning of HTA agencies and their role in the reimbursement system. All countries covered have an agency responsible for HTA evaluation, with only a few exclusively evaluating pharmaceuticals. Historically in many countries, Poland is a model example, initial role of the agency evolved and more responsibilities were added. National evaluations are conducted by all agencies, with some also performing regional assessments. The agencies are independent and some also serve as regulatory bodies for reimbursement decision making. The HTA assessment criteria reviewed in most countries include unmet medical needs, degree of innovation, clinical effectiveness, and safety profile. Most agencies also consider cost-effectiveness and impact on the payer’s budget. Only three countries explicitly include ethical, social, legal, and organizational aspects in their evaluation, indicating a lack of emphasis on these criteria by most HTA agencies. The methods and analytical techniques used in HTA are generally consistent across all agencies reviewed. The cost-effectiveness threshold is a key differentiator between agencies, with nearly half of the countries not having an officially established value for cost-effectiveness.Established in 2005, the EUnetHTA initiative, which mission is to foster cooperation among European institutions involved in HTA assessment, confirms the convergence of the various HTA agencies’ approaches on the issue of health technology assessment, especially clinical evaluation. The cooperation is expected to be beneficial from a European as well as a national and regional perspective, with the overarching goal of improving access to therapies . An apprSome authors point out that although countries implement HTA, the way in which this assessment fits into the reimbursement process  differs significantly from one system to another . The revReimbursement systems were initially introduced to enhance patients’ access to effective therapies. However, these systems often lack consistency in how they organize and appreciate the various stages involved. As a result, the decision-making process can become heterogeneous and less transparent.While many systems utilize HTA to inform reimbursement decisions, the implementation of HTA itself is not uniform. The inconsistency may stem from the nature of the final decision-making process, which can either be a logical, step-by-step, multi-stakeholder-driven, integrated, and longitudinal process or a decision made independently by the responsible body , based on recommendations or statements from other auxiliary bodies.Undoubtedly, these different arrangements can lead to variations in the time it takes to reach a decision and the genuine access patients have to drugs. It should be noted, however, that the time that elapses between registration and the availability of a drug to patients is long not only due to delays caused by the institutions involved in the reimbursement process but may also be the result of the strategy adopted by the pharmaceutical company, which begins the process not necessarily at the same time in each country, not always immediately after obtaining marketing authorization.1) The main limitation is that only 13 countries were included in the analysis, but this is determined by Polish HTA agency practice on referring to selected agencies. As AHTAPol does not refer to agencies in CEE countries we did not include them, it can be recognized as excused approach, but certainly, CEE countries could serve as more relevant in the context of available resources and shared challenges. 2) Only publicly available data were used in the publication, making it impossible in some cases to find detailed information about the reimbursement process or the HTA agency and its health technology assessment process. 3) Dynamic settings and evolution of the systems make it difficult to study and provide no time-sensitive conclusions. To prevent this, we also verified the information available on the websites of the various HTA agencies. 4) Due to the use of published data only, it is possible that the correlation between the various aspects of HTA and their impact on the final drug reimbursement decision are not fully captured. 5) It should be noted that the real access to drugs is not solely determined by the reimbursement decision, as it serves as limited proxy and falls outside the scope of this paper.


To diagnose an illness in healthcare, doctors typically conduct physical exams and review the patient's medical history, followed by diagnostic tests and procedures to determine the underlying cause of symptoms. Chronic kidney disease (CKD) is currently the leading cause of death, with a rapidly increasing number of patients, resulting in 1.7 million deaths annually. While various diagnostic methods are available, this study utilizes machine learning due to its high accuracy. In this study, we have used the hybrid technique to build our proposed model. In our proposed model, we have used the Pearson correlation for feature selection. In the first step, the best models were selected on the basis of critical literature analysis. In the second step, the combination of these models is used in our proposed hybrid model. Gaussian Naïve Bayes, gradient boosting, and decision tree classifier are used as a base classifier, and the random forest classifier is used as a meta-classifier in the proposed hybrid model. The objective of this study is to evaluate the best machine learning classification techniques and identify the best-used machine learning classifier in terms of accuracy. This provides a solution for overfitting and achieves the highest accuracy. It also highlights some of the challenges that affect the result of better performance. In this study, we critically review the existing available machine learning classification techniques. We evaluate in terms of accuracy, and a comprehensive analytical evaluation of the related work is presented with a tabular system. In implementation, we have used the top four models and built a hybrid model using UCI chronic kidney disease dataset for prediction. Gradient boosting achieves around 99% accuracy, random forest achieves 98%, decision tree classifier achieves 96% accuracy, and our proposed hybrid model performs best getting 100% accuracy on the same dataset. Some of the main machine learning algorithms used to predict the occurrence of CKD are Naïve Bayes, decision tree, K-nearest neighbor, random forest, support vector machine, LDA, GB, and neural network. In this study, we apply GB (gradient boosting), Gaussian Naïve Bayes, and decision tree along with random forest on the same set of features and compare the accuracy score. Nowadays, chronic kidney disease (CKD) is a rapidly growing disease, and millions of people die due to lack of timely affordable treatment. Chronic kidney disease patients belong to low-class and middle-classincome-generating countries , 2.In 2013, about one million people died due to chronic kidney disease . These fChronic kidney disease is a common type of kidney disease that occurs when both kidneys are damaged, and the CKD patients suffer from this condition for a long term. Here, the term kidney damage means any kidney condition that can cause improper functioning of the kidney. This could be caused by any disorder or due to lack of essentials like the glomerular filtration rate (GFR) reduction . Our proMachine learning is gaining significance in healthcare diagnosis as it enables intricate analysis, thereby minimizing human errors and enhancing the precision of predictions. Machine learning algorithms and classifiers are now considered the most reliable techniques for the diagnosis of different diseases like heart disease, diabetes, tumors disease, and liver disease predictions .Different machine learning algorithms used the Naïve Bayes, SVM, and the decision tree for the classification purpose, while random forest, logistic regression, and linear regression were used for the regression purpose in the medical fields for the prediction. With the efficient use of these algorithms, the death rate can be minimized due to early-stage diagnosis and patients can be treated timely. Along with maintaining the clinical symptoms, chronic kidney disease patients should include physical activities in daily life. They should exercise, drink water, and avoid junk food. The common symptoms of chronic kidney disease are shown in This article delivers an overview and analysis subsequently followed by an implementation and evaluation of the machine learning classifiers used in CKD diagnosis. Further, this article discusses the importance of machine learning classifiers in healthcare and explains how these can make more accurate predictions. The core objective of this article is to propose and implement a hybrid machine learning prediction model for chronic kidney disease where due importance is given to accuracy. In this article, we have analyzed the accuracy of same dataset with respect to different machine learning algorithms and compared their accuracy score so as to get a better model. Our focus remains on the solution of overfitting problem using cross-validation while achieving the highest accuracy to build a best hybrid model from the combination of available popular machine learning classifiers such as decision tree, gradient boosting, Gaussian Naïve Bayes, and gradient boosting. The ultimate goal is to deliver an accurate and effective treatment to CKD patients at a reduced cost. Before we proceed further, we need to know little more about common diseases of the kidney. In The remaining portion of the article is organized as follows. Section 2 contains the literature survey along with the tabular comparison of the different machine learning algorithms used and an analysis of the results. Section 3 contains the proposed methodology. Section 4 contains the dataset details. Section 5 contains results and discussion. This section covers research work related to algorithms and assesses some algorithms based on their accuracy. In research work , the datIn , the autIn the paper , they chIn , the autAs we have already seen, there are different machine learning prediction models and learning programs available to assist practitioners. In , they usIn , a machiThe kidneys play a vital role in maintaining the body's blood pressure, acid-base sense of balance, and electrolyte sense of balance, not only needed to filter toxins from the body. Malfunction is accountable for insignificant to mortal illnesses, in addition to dysfunction in the other body organs. Therefore, researchers all over the world have dedicated themselves for finding techniques to accurately diagnose and effectively treat chronic kidney disease. As machine learning classifiers are increasingly used in the medical field for diagnosis, now CKD is also included in the list of diseases that could be predicted using machine learning classifiers. The research to detect CKD with ML algorithms has enhanced the procedure and consequence accuracy progressively. They proposed the random forest classifier (99.75% accuracy) as the maximum efficient classifier among all other classifiers. The study demonstrates the effective handling of missing values in data through four techniques, namely, mode, mean, median, and zero-point methods. It also evaluates the performance of machine learning models under two scenarios, with and without tuning the hyperparameters, and observes significant improvement in the classifiers' performance, which is visually presented through graphs .Overall, the motive of the study is to examine the applicability of specific supervised machine learning classifiers in the field of bioinformatics and offer their compatibility in detecting several serious diseases such as the diagnosis of CKD at an early stage .They built an updated and proficient machine learning (ML) application that can perceptually perceive and predict the state of chronic kidney disease. In this work, the ten most important machine learning methods for predicting permanent kidney disease were considered. The level of accuracy of the classification algorithm we used in our project is as good as we wanted.For the prediction of disease, the first most essential step is to detect the disease that is costly in developing countries like Pakistan and Bangladesh. The people of these countries mostly suffer from this. Currently, CKD patient proportion is increasing rapidly in Pakistan and Bangladesh. So, in that article, the authors tried to develop a system that helps in predicting the risk of CKD. In the proposed model, they used and processed UCI datasets and real-time datasets and tried to deal with missing data and trained the model using random forest and ANN classifiers. Then, they implemented these two algorithms in the Python language. The accuracy they got with the random forest algorithm is 97.12% and that with ANN is 94.5%, which is relatively very good. By use of this proposed method, risk prediction of CKD at an early stage is possible.In , the aut  Author: this contains the names of the authors of each article along with the reference.  Year: this column provides the year of the paper's publication.  Input data: this column shows the type of dataset that was used as input for the machine learning classifiers.  Disease type: This section shows the type of disease that was predicted by using different classifiers. It shows the best classifier found in the research paper, which is the classifier with the maximum accuracy.  Classifiers: this column signifies the different machine learning classifiers that were used in the research and the comparison between them.  Tool: The column represents the programming language or the framework that was used in building the model. The researchers used these tools to preprocess the input data, then create a prediction model, and finally go to the testing stage.  Cross-validation: this column gives information about the validation of the classifiers and makes a comparison of different research papers regarding folds of cross-validation used.  Accuracy: The accuracy of the outcomes of the recommended model is represented in this column. If the article crisscrosses a comparison, the accuracy column only contains the accuracy percent of the best classifier confirmed by the author.In this section, a comprehensive comparison of the state of the art is presented in the form of a table. The evaluation is formed in the aspect of accuracy, which can be comprehended in The machine learning algorithms that we analyzed from the above literature are listed in k-fold technique and design our model in such a way that it can reduce the problem of overfitting along with achieving the highest accuracy. The classifiers are discussed as under.The proposed hybrid model is implemented in Python with pandas, sklearn, Matplotlib, Plotly, and other essential libraries. We have downloaded the CKD dataset from the UCI repository. The dataset contains two groups (CKD represented by 1 and non-CKD represented by 0) of chronic kidney disease in the downloaded information. The machine learning algorithm that has best accuracy is selected for analysis and implementation so that repeated results are produced. We have also developed a hybrid model based on knowledge that we gained during the analysis and implementation. The hybrid model consists of Gaussian Naïve Bayes, gradient boosting, and decision tree as base classifiers and random forest as a meta classifier. We have selected the tree-based machine learning algorithms for achieving the highest accuracy, while at the same time, it can handle the overfitting problem. In this paper, we detect the outliers with the violin plot as shown in The NB classifier is related to the group of probabilistic classifiers and is constructed on the basis of the Naïve Bayes (NB) theorem. It takes up vigorous independence between the component's/features, and it contains the most crucial part of how this classifier creates forecasts. It can be built easily and is appropriately used in the medical field for the prediction of different diseases .The decision tree classifier has a tree-like configuration or flowchart-like construction. It consists of subdivisions, leaves/child nodes, and a root/parent node. Here inner nodes comprise the features, whereas the subdivisions epitomize the outcome of every check on every node. Decision tree is one of the commonly used classifiers for classification determination because it does not need abundant information in the field or place constraints for it to work .n tree) used by the random forest classifierNumber of decision trees (Size of the minimum node in the treesm try). Here, m is the number of attributes.Number of attributes employed in splitting every node for every tree  is the most effective algorithm among the other machine learning algorithms. In prediction and probability estimations, random forest (RF) algorithm has been used. Random forest (RF) classifier consists of many decision trees. Tin Kam Ho of Bell Labs introduced the concept of random forest in 1995, where each decision tree casts a vote to determine the object's class. The RF method is the combination of both bagging and random selection of attributes. Random forest classifier has the three hyperparameter tuning values .Number oFor ensemble learning algorithms, the random forest is the most appropriate choiceFor large datasets, random forest classifier performs wellRandom forest (RF) is able to handle hundreds of input attributesRandom forest can estimate which attributes are more important in classificationMissing value can be handled by using random forest classifierRandom forest handles the balancing error for class in unbalanced datasetsSome of the advantages of the random forest classifier are listed as follows.Gaussian Naïve Bayes (GNB) calculated the mean and standard deviation of each attribute at the training stage. To calculate the probabilities for the test data, mean and standard deviation were used. Due to this reason, some values of attributes are too big or too small from the value of the mean calculated. It affects the classifier performance when testing data patterns have those attribute values and gives sometimes wrong output labels .We use the concept of stacking for our hybrid model. As a type of ensemble technique in stacking, multiple classification models were combined with a main/meta classifier. One after the other, multiple layers were placed, where the models pass their predictions, and the upper most layer model makes decisions on the base of the combination of different models as a base model. The models in the low layer get attributes as input from the original data. The topmost layer of the model gets output from the lower layers and gives the results as a final prediction. The stacking technique involves using multiple independent machine-learning models as input to process the original data. After that, the meta classifier is used to predict the input along with the output of each machine learning model and individual algorithm's weights are estimated. The algorithms that are performing best are selected, and others having low performance are removed. In this technique, multiple classifiers as base model are combined and then, by using different machine learning algorithms, are trained on the same dataset through the use of a meta-classifier .  FigureCollect the data of CKD from UCI repositoryExploratory data analysis (EDA) is performed on that datasetThis dataset is split into two parts: test data and train dataApply the cross-validation of 10 foldsTrain the base models Gaussian Naïve Bayes, gradient boosting, and decision tree with the train set giving the predictions as M1, M2, and M3, respectivelyThe output of the base models M1, M2, and M3 and test set data serve as input for random forest as input for trainingOnce the random forest gets trained, it gives the prediction on the basis of training dataset and the output predictions of the base modelsThe execution of the model with the sequence of the steps is given below:We implemented four machine learning algorithms that are decision tree, gradient boosting, Gaussian Naïve Bayes, and random forest. We applied stacking classifiers to build the hybrid model that combines these four algorithms.We analyzed the accuracy of the same dataset with respect to different machine learning algorithms and compared their accuracy score to get the best modelWe implemented a stacking classifier technique to build a new model with improved accuracyIn this study, we have considered the UCI CKD dataset, and this dataset is split into two parts. 80% of data is used for training purposes as an input to the machine learning algorithms. We exploited the Gaussian Naïve Bayes, gradient boosting, decision tree, and stacking classifier with random forest algorithm which was used to predict the chronic kidney disease for 20% test data as input and plotted the predicted values and compared their values. Our proposed methodology has the following advantages.We selected 14 attributes from the dataset that we are using from the UCI repository dataset of chronic kidney disease as input features as shown in To identify the number of chronic kidney disease patients and the number of healthy ones, we performed the visualization on the CKD dataset, which can be seen in the histogram plot in The Pearson correlation feature selection method is used to get the best combination of features for the prediction of chronic kidney disease. The correlation of the 14 attributes and 1 output label is presented in When we go from the exploratory data analysis stage to the pair plot visualization, it is observed to be very helpful as it gives the data that can be used to find the relationship between attributes for both the categorical and continuous variables. We import the Seaborn library to get pair plot. The information about all the attributes is in one picture and is clear. The statistical information is in attractive format represented with pair plot as shown in The violin plots are used for all the attributes in exploratory data analysis that are used in the hybrid model. These can give additional useful information like density trace and distribution of the dataset. The violin plots give the whole range of dataset which cannot be shown by box plot. The violin plots of all 14 attributes are given in Machine learning algorithms such as gradient boosting, Gaussian Naïve Bayes, decision tree, and random forest classifier were used in the proposed hybrid model. These different machine learning classifiers were used as a combination for the chronic kidney disease predictions. This also overcomes the overfitting problem and results in higher accuracy. In order to improve accuracy and to come up with a novel approach as compared to the existing work, we have implemented the proposed hybrid model with the best combination of GB, GNB, and decision tree, along with the random forest classifiers –27. The In order to find the contributions to the development of prediction models for chronic kidney disease, a regional basis analysis is performed. As discussed in the Introduction section that the developing countries' population suffers more from chronic kidney disease, it was observed that most of the research work is performed in developing countries. A summary of this region-wise contribution is presented in Chronic kidney disease is considered as one of the prominent life-threatening diseases in the developing world. The most obvious cause seems to be lack of physical exercise. The medical practitioners used a number of diagnosis processes and procedures, where machine learning is the recent development. In this paper, we have selected machine learning because in terms of accuracy, it performs better as compared to other available approaches. In this article, we have used the Pearson correlation feature selection method and applied the same on machine learning classifier. GB, GNB, decision tree, and random forest are the base classifiers for the stacking algorithm, whereas these are implemented with the cross-validation on the basis of accuracy score. In this study, we evaluated these algorithms on the same dataset. Furthermore, we have used dataset of CKD from the UCI directory that contains 14 attributes and 400 instances. On the basis of these attributes, our proposed stacking model is able to predict whether the person is a CKD patient or not with 100% accuracy. Best features are selected using the Pearson correlation method, and the stacking algorithm is implemented with the best machine learning classifiers. The cross-validation enhances the performance of the stacking model. As we have worked on the chronic kidney disease data of the binary group, the stacking algorithm performs better with these combinations of algorithms. We can implement the stacking technique for the prediction of other diseases to get better accuracy score.


The selection of ornamental stones for specific applications requires technical guidance since it should be based on the durability, service life, and aesthetic value of the stones. In most cases, these fundamentals provide quantitative data on the usability and performance of ornamental stones. The present study attempts to put forward a quantitative classification system for natural stone products concerning critical rock properties. For this purpose, fundamental physical and mechanical rock properties are listed based on European standards. Then, minimum limit values are proposed for different applications of natural stone products based on retrospective analyses of numerous ornamental stone applications. The suggested limit values based on several physical and mechanical rock properties can guide relevant engineers to initially consider possible rock types for use as natural stones in a wide range of applications. In this context, it is believed that the present study contributes to the natural stone industry by discussing the minimum limit values for the consideration of a wide range of rock types possibly usable in the dimension stone industry. Natural stones have been widely used for architecture and construction purposes since ancient times ,2,3. In The development of the construction and building industry is highly associated with the supply of high-quality raw materials. Nevertheless, compared to other commonly used building materials , the efficiency and productivity of ornamental stone utilisation and treatment processes are quite limited ,12. The On the other hand, the durability and attractiveness of ornamental stones make them eligible to be among the best alternatives for designing construction, building and other engineering projects ,7.Selecting the appropriate rock type for relative engineering purposes and making an exact decision regarding its usability and employability can be challenging and require effort on the part of homeowners and contractors . For thiIt should be noted that this article might be declared a modest initiation of a scientific discussion on the development of a comprehensive evaluation methodology to classify and assess rock types used in the dimension stone industry.In the literature, one may notice an interchangeable use of the terms: dimension stones, natural stones, and ornamental stones ,18,19,20Therefore, ornamental stones seem to be the most pertinent term in the context of the requirements for natural stone products. Given the technical value of stone material, besides their economic value, ornamental stones also have a decorative function . In pracThe quality requirements concerning the colour of stone are stringent. The colour should be as uniform as possible across the entire deposit. If a stone is classified as a one-coloured type, stripes, inclusions, or veins of a differing colour cannot be accepted for a first-class stone. However, if the stone is classified as multi-coloured, appropriate variation in colours is required. In this case, the colour and structure of the stone must be homogeneous enough so that its market value can be considerable . AlthougIn the context of stone selection for a specific application, it is highly recommended to carefully consider the characteristics of rocks in a detailed manner. Carvalho et al.  emphasisAs a natural material, stone can be one of the most difficult building materials to properly evaluate, select, and specify . AladejaSince natural stones are some of the most challenging building materials, whose technical properties highly affect the durability of a structure, the selection of the proper rock type as a natural stone is of prime importance. Considering the physical and mechanical properties of ornamental stones, it is possible to see their strong interdependence ,45,46,47Rocks used as building materials must satisfy high quality standards to ensure the long-term durability of engineering structures ,42. TheyOrnamental stones are widely used in architecture or construction  and, in Stone is widely used for paving and road surfaces in the form of slabs, setts, or kerbs due to its high durability. According to EN 1341 , an exteMasonry units manufactured from natural stone f are eleThe other group of natural stone products is dimensional stonework, where stone elements can mostly be used in the building sector. These include curved stones or stone elements with three-dimensional stone shapes, including flat stone elements g,h whichThere is no doubt that the uses and applications of stone are much broader. The other most common use of ornamental stones is for kitchen and bathroom countertops. In any urban area, ornamental stones are used for entrances, plinths, outdoor benches, etc. They are also used as basins, altars, and ornate fixtures in churches.Ornamental stones are used for indoor and outdoor furniture. In addition, ornamental stones are considered ideal materials for fireplaces ,68.Regulation (EU) No. 305/2011 of the European Parliament and of the Council of 9 March 2011 laying down harmonised conditions for the marketing of construction products and repealing Council Directive 89/106/EEC (a text with EEA relevance)  defines Harmonised standards for product requirements and test methods for natural stone are widely used in Europe. The requirement standards for natural stones define the properties and methods for their identification depending on the application. Based on the properties of the natural stone, the manufacturer prepares a declaration of performance when it is placed on the market and marks the product with a CE mark. It must be emphasised that it is the obligation of the manufacturer of natural stone products to prepare a declaration of performance. However, despite this obligation, it is not common practice. This is due to a lack of knowledge or the high cost of performing tests to determine the properties of ornamental stones. Companies operating in the extractive industry of ornamental stones are small and medium-sized enterprises with strong links within their own commercial-activity circles . Thus, iSince ornamental stones have a wide range of applications, the current standards only provide guidelines for the general use of these natural materials . AccordiA different solution for determining the requirements for natural stone products is represented by American standards. These standards refer to the lithological type of rock and define the minimum values of stone properties . It seemThe requirements for the limit values of ornamental stone properties should be defined in the technical specifications of the investment in which the stone product should be used. Since there are no minimum requirements for ornamental stones based on their physical and mechanical properties, in this section, several guidelines for ornamental stones are proposed based on previous experiences of a wide range of natural stone applications.Different approaches to classifying and assessing the quality and durability of ornamental stones become complicated for different products of building materials. In addition, different test methods based on American and European standards are hardly comparable in assessing the physical and mechanical properties of rocks.Based on the above explanations, a general quantitative classification for ornamental stones is listed in It should be noted that in the quantitative classification for ornamental stones  and in tIt is essential that the selected stone be sound, durable, and able to withstand external factors during its service life . The quaThe authors suggest that extensive research is needed to define the minimum requirements for ornamental stones based on the fundamental rock properties. The proposed classification system defined in Stones probably will not meet all the requirements at the same time, i.e., in some cases, a stone material is defined as high quality based on a rock property, but it can be defined as a low-quality material based on the other rock properties. The use of ornamental stones should be associated with the actual critical factors affecting the durability of the stone. For instance, ornamental stones used for cladding purposes should be designed and applied safely where flexural strength values are the vital factors, while ornamental stones used for paving purposes should be resistant to abrasion and fragmentation. Whenever the safety and durability of a structure are more significant, minimum requirements should be taken into account. Furthermore, the public/private sectors should consider the appropriate use of ornamental stones. In outdoor utilisations, ornamental stones with low abrasion resistance should not be used, since, after a short time, the slip risk is increased and the safety of pedestrians is compromised. However, in a private backyard, the same material can be used when the owner conscientiously assumes the risk.Another topic for discussion is the use of hydrophobic and consolidant products, which can help stabilise the physical–mechanical properties of ornamental stones in the long term. These products can widen the range of applications for different ornamental stones due to the improvement of their physical–mechanical properties. For instance, if the porosity is lowered by using a hydrophobic treatment, the physical properties are improved. On the other hand, the use of consolidants can improve the mechanical properties. It must be emphasised that the selection of hydrophobic and consolidating products must be undertaken carefully because an incorrect choice can lead to higher deterioration of the ornamental stones. Failure to follow this practice is a major engineering error.In this study, critical rock properties and their testing methods based on European standards are summarised to assess the quality and durability of ornamental stones. Qualitative evaluation of rock properties is extremely difficult and tricky. The different approaches of developers and designers of building projects indicate the problems of selecting the appropriate stone materials for their applications.It is believed that the suggested minimum guidelines can guide relevant engineers to initially select possible rock types for use as natural stones in a wide range of applications. The presented quantitative classifications and average limit values of the basic properties of ornamental stones are the beginning of a wide scientific discussion in the development of fundamental criteria to select appropriate stone materials in the natural stone industry.-In European conditions, the standards for natural stone requirements do not indicate limit values based on physical and mechanical properties.-Physical and mechanical properties should be some of the fundamental criteria for the selection of ornamental stones depending on different applications.-A broader discussion should be initiated on the identification and classification of ornamental stones. The present study, in this context, may initiate a scientific discussion on defining the minimum requirements of ornamental stones based on their physical and mechanical properties. However, additional studies and field experiences are still required to improve the suggested minimum requirements.The main conclusions can be drawn as follows:-Redesign based on the lithological variances  of rocks.-Inclusion of the use of hydrophobic and consolidation products to stabilise the physical and mechanical properties of rocks in case of exposure to different environmental factors.Further studies should focus on the following considerations: