Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree
- Authors
- Kim, Jaekwon; Lee, Jongsik; Lee, Youngho
- Issue Date
- Jul-2015
- Publisher
- KOREAN SOC MEDICAL INFORMATICS
- Keywords
- Heart Disease; Decision Tree; Fuzzy Logic; KNHANES; Data Mining
- Citation
- HEALTHCARE INFORMATICS RESEARCH, v.21, no.3, pp.167 - 174
- Journal Title
- HEALTHCARE INFORMATICS RESEARCH
- Volume
- 21
- Number
- 3
- Start Page
- 167
- End Page
- 174
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/10368
- DOI
- 10.4258/hir.2015.21.3.167
- ISSN
- 2093-3681
- 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.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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