According to World Health Organization, Heart diseases are the leading cause of death globally. Early prediction of this disease is now important to reduce the number of deaths due to this disease. The research paper aims to build a model based on clustering PAM and Apriori algorithm for predicting heart diseases in R language. In this article, clustering algorithms, K-means and PAM are compared and best among them is chosen with optimal number of clusters and each cluster is fed to the Apriori algorithm and a classifier is built based on association rules and compared the classifier’s accuracy by varying support values for both the cluster and the one with the best accuracy is chosen.

Topics
Machine learning, Diseases and conditions, Scientific society and organization
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