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：2016
卷号：88
期号：3
出版社：Journal of Theoretical and Applied
摘要：Heart disease is one of the significant reasons of death and the progress of which is rampant all over the globe. Blood vessels carry blood with oxygen to all the cells in the body. It is a common reason that, Cholesterol and other substances can be deposited in blood vessels which block blood vessels and that no blood and oxygen can get to heart. This leads to heart disease. Several works have been made to predict the heart disease in different methods. The main aim of this paper is to predict heart disease using Multiple Regression and Bayesian Regularization methods and compare the results of these models. Multiple Regression is one of the strong model used for prediction and it shows the association between input variables and output variable. It predicts the output variable based on the relationship between one or more input variables and target variable. Bayesian regularization is a statistical model which process nonlinear dataset. It increases the generalization capability and decreases squared errors. Bayesian regularization works on with large inputs efficiently. The results are calculated using Multiple Regression and Bayesian Regularization methods and predicted the heart disease. The results of Multiple Regression and Bayesian Regularization are compared and it is observed that the results generated from Bayesian Regularization are more accurate than multiple regression model.
