期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：79
期号：2
出版社：Journal of Theoretical and Applied
摘要：Disease is a term for defining a large amount of healthcare conditions that affects part or all of an organism. Knowledge of various disease symptoms and signs favorable for disease development is necessary to optimize a disease forecaster. Different data mining techniques such as Naive Bayes, Decision Tree, Linear Regression and Association Rule are used to predict the heart disease. Data mining techniques in all disease diagnosis applied over all disease treatment dataset investigate if hybrid data mining techniques can achieve equivalent (or better) results in identifying suitable treatments as that achieved in the diagnosis. In this paper, our work is to more accurately predict the presence of heart disease with added attributes of the disease and using association rules. Final results show that association rule implemented on dataset produces better accuracy.
关键词：Artificial Neural Network (ANN); Cleveland Heart Disease Data Set (CHDD); k-Nearest Neighbor (KNN); Heart Disease Prediction and Diagnosis; Knowledge Discovery in Database (KDD)
