Emerging patterns based methodology for prediction of patients with myocardial ischemia

Authors:
Minghao Piao;Heon Gyu Lee;Gyo Yong Sohn;Gouchol Pok;Keun Ho Ryu
Affiliations:
Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea;Poster & Logistics Technology Research Dept., Electronics & Telecommunications Research Institute, Daejeon, Korea;Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea;Department of Computer Science, Yanbian University of Science & Technology, Yanji, China;Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

Citing 10
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Abstract

Heart disease is the one of the significant health problem in the world. Recently, most serious problem caused by it is that the patient becomes younger. Therefore, it is very important and necessary to find the early symptoms of heart problems for better treatment and effective methodology for predicting the disease. Data mining is the one of the efficient approaches. However, there are still some tasks have to be solved. One is that the result should make it easy to explain the relationship between class label and predictors for the heart disease data. In this paper, redefined T-tree algorithm is used to mine the emerging patterns to perform the work and solve the problem. Also, the aggregate score is considered to build classifier for the prediction work. The algorithms CMAR, CPAR, C4.5 and our method are applied to the dataset and the proposed method shows the better accuracy than others (The accuracy is between 75% to 85%).