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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.1
- 수록면
- 167 - 174 (8page)
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초록· 키워드
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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.
목차
등록된 정보가 없습니다.
참고문헌 (23)
참고문헌 신청
Akay M / 1992 / Noninvasive diagnosis of coronary artery disease using a neural network algorithm / Biol Cybern 67 (4) : 361 ~ 367
Anooj PK / 2012 / Clinical decision support system : risk level prediction of heart disease using decision tree fuzzy rules / Int J Res Rev Comput Sci 3 (3) : 1659 ~ 1667
Barman M / 2012 / A fuzzy rule base system for the diagnosis of heart disease / Int J Comput Appl 57 (7) : 46 ~ 53
Freund Y / 1999 / The alternating decision tree learning algorithm / Proceedings of the 16th International Conference on Machine Learning (ICML’99) : 124 ~ 133
Jung H / 2014 / Evolutionary rule decision using similarity based associative chronic disease patients / Cluster Comput 18 (1) : 279 ~ 291
Anooj PK / 2012 / Clinical decision support system : risk level prediction of heart disease using decision tree fuzzy rules / Int J Res Rev Comput Sci 3 (3) : 1659 ~ 1667
Barman M / 2012 / A fuzzy rule base system for the diagnosis of heart disease / Int J Comput Appl 57 (7) : 46 ~ 53
Freund Y / 1999 / The alternating decision tree learning algorithm / Proceedings of the 16th International Conference on Machine Learning (ICML’99) : 124 ~ 133
Jung H / 2014 / Evolutionary rule decision using similarity based associative chronic disease patients / Cluster Comput 18 (1) : 279 ~ 291
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