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The risk of developing early heart disease is always prominent. In fact, according to the Central of Disease Control and Prevention (CDC), Cardiovascular disease accounts for nearly 801,000 deaths in the US. In this paper, we present a machine learning and decision based system for heart disease prediction. We validate our approach on the Heart Disease Dataset gathered from the UCI machine learning repository. Cleveland, Hungarian and Switzerland datasets are combined and trained with the use of SVM and Naïve Bayes algorithms. Comparison and analysis with other models show that the accuracy of the proposed approach is 87% and 86% for SVM and Naïve Bayes respectively, which is much higher comparing to the existing approaches that use a small subset of data and no imputation technique.

A Machine Learning Approach: Using Predictive Analytics to Identify and Analyze High Risks Patients with Heart Disease