Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree
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초록
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.
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.
참고문헌 (23)
- "The world health report 2010" (2015)[URL]
- Liu K, "Healthy lifestyle through young adulthood and the presence of low cardiovascular disease risk profile in middle age : the Coronary Artery Risk Development in(Young)Adults(CARDIA)study", Circulation, 125(8), pp.996~1004 (2012)
- Wilson PW, "Prediction of coronary heart disease using risk factor categories", Circulation, 97(18), pp.1837~1847 (1998)
- 이도영, "한국인 남성에서 American Heart Association", National Heart, Lung, and Blood Institute와 International Diabetes Federation 대사증후군 진단 기준에 따른 심혈관질환 예측률의 비교, 2008, pp.32(4), 317~327[DOI]
- Kim JK, "Adaptive mining prediction model for content recommendation to coronary heart disease patients", Cluster Comput, 17(3), pp.881~891 (2014)
- 송미화, "A Multi-Classifier Based Guideline Sentence Classification System", Healthcare Informatics Research, 17(4), pp.224~231 (2011), 대한의료정보학회
- Peyman Rezaei Hachesu, "Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients", Healthcare Informatics Research, 19(2), pp.121~129 (2013), 대한의료정보학회[DOI]
- Palaniappan S, "Intelligent heart disease prediction system using data mining techniques", Proceedings of IEEE, 2008, 108~115
- Anooj PK, "Clinical decision support system : risk level prediction of heart disease using decision tree fuzzy rules", Int J Res Rev Comput Sci, 3(3), pp.1659~1667 (2012)
- Khatibi V, "A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment", Expert Syst Appl, 37(12), pp.8536~8542 (2010)
- Krishnaiah V, "Emerging ICT for bridging the future: Proceedings of the 49th Annual Convention of the Computer Society of India (CSI)", pp.371~384 (2015), Springer International Publishing
- Jung H, "Evolutionary rule decision using similarity based associative chronic disease patients", Cluster Comput, 18(1), pp.279~291 (2014)
- Barman M, "A fuzzy rule base system for the diagnosis of heart disease", Int J Comput Appl, 57(7), pp.46~53 (2012)
- Papageorgiou EI, "A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques", Appl Soft Comput, 11(1), pp.500~513 (2011)
- Kim JK, "IT Convergence and Security 2012", pp.1015~1020 (2013), Springer Netherlands
- "The 6th Korea National Health & Nutrition Examination Survey 2013 (KNHANES VI)" (2015)[URL]
- 박명화, "Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly", Healthcare Informatics Research, 20(1), pp.30~38 (2014), 대한의료정보학회[DOI]
- Son EJ, "IT Convergence and Security 2012", pp.1021~1025 (2013), Springer Netherlands
- Zhu W, "Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations", Proceedings of Northeast SAS Users Group (NESUG) 2010: Health Care and Life Sciences, pp.1~9 (2010)
- Akay M, "Noninvasive diagnosis of coronary artery disease using a neural network algorithm", Biol Cybern, 67(4), pp.361~367 (1992)
- Sengur A, "Support vector machine ensembles for intelligent diagnosis of valvular heart disease", J Med Syst, 36(4), pp.2649~2655 (2012)
- Laurent S, "Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients", Hypertension, 37(5), pp.1236~1241 (2001)
- Freund Y, "The alternating decision tree learning algorithm", Proceedings of the 16th International Conference on Machine Learning (ICML’99), pp.124~133 (1999)