Development of Prediction Model for Heart Disease by Combining Clustering and Classification Techniques

Abstract

The concerning trends in deaths related to heart disease some measures need to be in place to ensure early treatment and diagnosis of the disease. Therefore, one of the way can be done is by leveraging the abundance of medical data available. Advancement in technology today has improved the availability and accessibility huge amounts of valuable data and it only makes sense for us to explore the opportunities that lie in the data that could possibly save lives and reduce costs. Thu, this study aims to do that with the help of classification and clustering data mining techniques to predict heart disease based on some key indicators of the disease. Studies show that applying classifiers on clustered data can improve the performance of algorithms. Hence, this method will be explored in this study using the Naïve Bayes, Decision Tree and Random Forest classifiers together with both K-Means Clustering and Density-Based Clustering on the data analysis using tool WEKA. The performance of the each model will be measured and compared against each other using accuracy, precision, recall, specificity, AUC and model build time. Thus, this paper will focused on development of prediction model for heart disease by combining clustering and classification techniques in detail.