|
KMID : 0603720150210030167
|
|
Journal of Korean Society of Medical Informatics
2015 Volume.21 No. 3 p.167 ~ p.174
|
|
|
Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree
|
|
Kim Jae-Kwon
Lee Jong-Sik
Lee Young-Ho
|
|
|
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.
|
|
|
KEYWORD
|
|
|
Heart Disease, Decision Tree, Fuzzy Logic, KNHANES, Data Mining
|
|
|
FullTexts / Linksout information
|
|
|
|
|
Listed journal information
|
|
  
|