Prediction of Heart Disease Using Random Forest and Feature Subset Selection

Abstract

Heart disease is a leading cause of death in the world. Heart disease is the number one killer in both urban and rural areas. Predicting the outcome of disease is the challenging task. Data mining can be can be used to automatically infer diagnostic rules and help specialists to make diagnosis process more reliable. Several data mining techniques are used by researchers to help health care professionals to predict the heart disease. Random forest is an ensemble and most accurate learning algorithm, suitable for medical applications. Chi square feature selection measure is used to evaluate between variables and determines whether they are correlated or not. In this paper, we propose a classification model which uses random forest and chi square to predict heart disease. We evaluate our approach on heart disease data sets. The experimental results demonstarte that our approach improve classification accuracy compared to other classification approaches, and the presented model can help health care professional for predicting heart disease.