Fuzzy association rule mining and classification for the prediction of XXXX in South Korea.

@entity311 @entity4002 @entity9680 @entity2286
BACKGROUND: @entity311 is the world's most prevalent @entity4002 . Accurate prediction of @entity311 outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. METHODS: We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a @entity311 prediction model. Future @entity311 cases are predicted as LOW, MEDIUM or @entity9680 , where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, @entity9680 is considered an outbreak. RESULTS: Model accuracy is described by Positive Predictive Value ( @entity2286 ), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model @entity2286 and Sensitivity are 0.842 and 0.681, respectively, for the @entity9680 classes. The F0.5 and F3 scores (which combine @entity2286 and Sensitivity) are 0.804 and 0.694, respectively, for the @entity9680 classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the @entity9680 class. For the MEDIUM class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. CONCLUSIONS: A previously described method for creating disease prediction models has been modified and extended to build models for predicting @entity311 . In addition, some new input variables were used, including indicators of intervention measures. The South Korea @entity311 prediction models predict LOW, MEDIUM or @entity9680 cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.
