ArticleHealthcare informatics research2015
Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree.
Article in Healthcare informatics research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors.Frontiers in digital health · 2025Article
- Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model.Brain sciences · 2023Article
- Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study.BMC medical informatics and decision making · 2022Article
- Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO.IET systems biology · 2020Article
- Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.Omics : a journal of integrative biology · 2020Review
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- Comparing different supervised machine learning algorithms for disease prediction.BMC medical informatics and decision making · 2019Article
- Predicting coronary artery disease: a comparison between two data mining algorithms.BMC public health · 2019Article
- Reconstruction error based deep neural networks for coronary heart disease risk prediction.PloS one · 2019Article
- A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction.Advances in preventive medicine · 2019Article
- Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease.Healthcare informatics research · 2016Article
- Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm.Healthcare informatics research · 2016Article
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Authors and funding
3 authors.
Funding
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Abstract
objectivesThe 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.
methodsA 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.
resultsThe rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction.
conclusionsThe 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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