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Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree

Jaekwon Kim1, Jongsik Lee1, Youngho Lee2

  • 1Department of Computer and Information Engineering, Inha University, Incheon, Korea.

Healthcare Informatics Research
|August 18, 2015
PubMed

Insights

This study developed a new coronary heart disease (CHD) prediction model for Koreans using fuzzy logic and decision trees. The model demonstrated improved efficiency compared to existing methods for CHD risk assessment.

Area of Science:

  • Cardiovascular Health
  • Artificial Intelligence in Medicine
  • Public Health Informatics

Background:

  • Coronary heart disease (CHD) prediction is crucial in Korea, yet research is limited.
  • A need exists for effective CHD prediction and classification methods tailored for the Korean population.

Purpose of the Study:

  • To develop a rule-based coronary heart disease (CHD) prediction model for Koreans.
  • To enhance CHD prediction accuracy by integrating fuzzy logic and decision tree techniques.

Main Methods:

  • A decision tree (Classification and Regression Tree [CART]) approach was employed to generate predictive rules.
  • Fuzzy logic was incorporated to address uncertainties inherent in coronary heart disease (CHD) prediction.
  • The model was trained using data from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI).

Main Results:

  • The developed model achieved an accuracy of 69.51%.
  • The receiver operating characteristic (ROC) curve value was recorded at 0.594.
  • The proposed fuzzy logic and CART-driven model demonstrated superior efficiency over alternative models.

Conclusions:

  • The integrated fuzzy logic and decision tree model offers a more efficient approach to coronary heart disease (CHD) prediction in Koreans.
  • The findings highlight the potential of AI-driven methods for improving cardiovascular disease risk assessment.
Abstract

Keywords:
Data MiningDecision TreeFuzzy LogicHeart DiseaseKNHANES

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