Heart Disease Prediction Using Machine Learning Techniques

Abstract

Heart disease is the leading cause of death in the developed world. Early detection of the heart disease can prevent death, as well as other disease that is related to it such as dementia. Therefore, studies in preventing the risks of having a stroke or heart attack required. Using machine learning techniques, the aim of this study is to evaluate the accuracy of supervised learning techniques in predicting heart disease based on the dataset obtained from University of California Irvine data repository. The result from this study shows that Naïve Bayes and Bayesian Network has better estimated accuracy in Weka for the data set, while both Bayesian Network and J48 may give useful insight with Weka generated visualization.