A total of 909 records were obtained from the Cleveland Heart Disease database. It has been
observed during the analysis that Naive Bayes appears to be most effective as it has the highest
percentage of correct predictions (86.53%) for patients with heart disease, followed by Neural
Network (85.53%) and Decision Trees. Decision Trees, however, appears to be most effective in
case of predicting patients with no heart disease,