Decision Tree Driven Rule Induction for Heart Disease Prediction Model: Korean National Health and Nutrition Examinations Survey V-1
Heart disease has the highest rates of death in non-communicable disease and there have been much research on heart disease. Even though there is recognition for importance of heart disease prediction, related studies are insufficient. Therefore, to develop heart disease prediction model for Korean, we suggest data mining driven rule induction for heart disease prediction in this paper. Proposed method suggest heart disease prediction model by applying decision tree driven rule induction based on data set from Korean National Health and Nutrition Examinations Survey V-1 (KNHANES V-1). The prediction model is expected contribute to Korea’s heart disease prediction.
KeywordsData mining Heart disease prediction Decision tree Rule induction KNHANES V-1
This work was supported by the R&D Program of MKE/KEIT [10032115, Development of Digital TV based u-Health System using AI].
- 1.World Health Organization (2010) The world health report 2008. http://www.who.int/whr/2008/whr08_en.pdf. Accessed Nov 2010
- 2.Wilson P, D’Agostino R, Levy D, Belanger A, Silbershatz H, Kannel W (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97:1837–1874Google Scholar
- 3.Do Young L, Eun Jung R, Eun Suk C, Ji Hoon K, Jong Chul W, Cheol Young P, Won Young L, Ki Won O, Sung Woo P, Sun Woo K (2008) Comparison of the predictability of cardiovascular disease risk according to different metabolic syndrome criteria of American heart association/national heart, lung, and blood institute and international diabetes federation in Korean men. J Diabetes Metab 32(4):317–327Google Scholar
- 5.Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using decision tree fuzzy rules. Int J IJRRCS 3(3):1659–1667Google Scholar
- 6.Korea Centers for Disease Control and Prevention (2010) 5th Korean national health and nutrition examinations survey (KNHANES V-1). Centers for Disease Control and Prevention, SeoulGoogle Scholar
- 7.Pagola M, Bustince H, Brugos A, Fernandez A, Herrera F (2011) A case study on medical diagnosis of cardiovascular diseases using a genetic algorithm for tuning fuzzy rule-based classification systems with interval-valued fuzzy sets. In: Proceedings of 2011 IEEE symposium on advances in T2FUZZ, pp 9–15Google Scholar