Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree / 대한의료정보학회지
Healthcare Informatics Research
;
: 167-174, 2015.
Article
in English
| WPRIM
| ID: wpr-34682
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Decision Trees
/
Nutrition Surveys
/
ROC Curve
/
Classification
/
Fuzzy Logic
/
Coronary Disease
/
Uncertainty
/
Data Mining
/
Dataset
/
Heart Diseases
Type of study:
Etiology study
/
Health economic evaluation
/
Prognostic study
/
Qualitative research
Country/Region as subject:
Asia
Language:
English
Journal:
Healthcare Informatics Research
Year:
2015
Type:
Article
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