Disease prediction via Bayesian hyperparameter optimization and ensemble learning.
OBJECTIVE
Early disease screening and diagnosis are important for improving patient survival. Thus, identifying early predictive features of disease is necessary. This paper presents a comprehensive comparative analysis of different Machine Learning (ML) systems and reports the standard deviation of the results obtained through sampling with replacement. The research emphasises on: (a) to analyze and compare ML strategies used to predict Breast Cancer (BC) and Cardiovascular Disease (CVD) and (b) to use feature importance ranking to identify early high-risk features.


RESULTS
The Bayesian hyperparameter optimization method was more stable than the grid search and random search methods. In a BC diagnosis dataset, the Extreme Gradient Boosting (XGBoost) model had an accuracy of 94.74% and a sensitivity of 93.69%. The mean value of the cell nucleus in the Fine Needle Puncture (FNA) digital image of breast lump was identified as the most important predictive feature for BC. In a CVD dataset, the XGBoost model had an accuracy of 73.50% and a sensitivity of 69.54%. Systolic blood pressure was identified as the most important feature for CVD prediction.
['Gao|Liyuan|L|', 'Ding|Yongmei|Y|']
D001499:Bayes Theorem D044963:Biopsy, Fine-Needle D001943:Breast Neoplasms D002318:Cardiovascular Diseases D019937:Diagnostic Techniques and Procedures D005260:Female D006801:Humans D000069550:Machine Learning D008954:Models, Biological D012680:Sensitivity and Specificity
Hyperparameter optimization Ensemble learning Feature selection Gain