Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree.
10.4258/hir.2015.21.3.167
- Author:
Jaekwon KIM
1
;
Jongsik LEE
;
Youngho LEE
Author Information
1. Department of Computer and Information Engineering, Inha University, Incheon, Korea.
- Publication Type:Original Article
- Keywords:
Heart Disease;
Decision Tree;
Fuzzy Logic;
KNHANES;
Data Mining
- MeSH:
Classification;
Coronary Disease*;
Data Mining;
Dataset;
Decision Trees*;
Fuzzy Logic*;
Heart Diseases;
Korea;
Nutrition Surveys;
ROC Curve;
Uncertainty
- From:Healthcare Informatics Research
2015;21(3):167-174
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. METHODS: A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. RESULTS: The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. CONCLUSIONS: The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.
