An Efficient Feature Selection Based Heart Disease Prediction Model

Abstract

Heart disease is one of the health concerns of humans. It has caused thousands sad demises of people early in their life. There are different kinds of heart diseases and each one has its symptoms and they are preventable or even curable if detected early. Therefore, early detection of heart disease is wiser way of diagnosing it. Fortunately, health data of a person is sufficient to detect the probability of heart disease accurately. This has motivated many researchers and academia investigating into data-driven approaches towards solution. Machine learning techniques that are part of Artificial Intelligence (AI) play key role in the prediction of heart diseases. The existing research on it revealed their utility in garnering Business Intelligence (BI) for making expert decisions. However, in terms of feature selection and improving performance of detection mechanisms there is need for further scope of the research. In this paper a novel feature selection algorithm named Entropy and Gain-based Feature Selection (EGFS) is proposed. The hypothesis “feature selection improves performance of heart disease prediction models” is evaluated using EGFS by applying it with state of the art machine learning methods like k-Nearest Neighbour (k-NN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Support Vector Machines (SVM). These methods are used to form heart disease prediction models. The empirical study revealed that the performance of the prediction models is improved with EGFS. The effectiveness of prediction models is enhanced with feature selection process.