Ensemble of Multiple Models For Robust Intelligent Heart Disease Prediction System

Abstract

Recently heart disease has become the most common fatal diseases in the world. Early stage detection and treatment can reduce the number of cardiac failures, mortality of heart disease and cost of diagnosis. The healthcare industry collects a huge amount of these medical data, but unfortunately, these are not mined. Discovery of hidden patterns and relationships from this data can help effective decision making to predict the risk of heart disease. The main objective of this research is to develop a Robust Intelligent Heart Disease Prediction System (RIHDPS) using some classification algorithms namely, Naive Bayes, Logistic Regression and Neural Network. This article reviewed the effectiveness of clinical decision support systems by ensemble methods of these three algorithms.