A Comparative Study on Different Machine Learning Algorithms for Achieving Accurate Prediction for Heart Diseases

Abstract

Over the years, heart diseases have become one of the most common causes related to death. Most of the time heart diseases are detected at the very last stage; therefore, an accurate prediction may reduce the catastrophe related to heart diseases. Heart-related diseases have a significant relationship with various health features including age, sex, heartbeat rate, blood pressure, cholesterol etc. In this context, four machine learning algorithms (e.g. Multiple Linear Regression, Decision Tree, Random Forest and Support Vector Machine) are applied on Cleveland heart disease dataset to analyze the comparative performance for achieving accurate prediction. The dataset contains thirteen health features, which have significant relations to heart disease. The best prediction has been achieved by the Random Forest algorithm, which is an ensemble version of the Decision Tree algorithm. To recapitulate the Random Forest algorithm outperformed other three algorithms followed by Support Vector Machine algorithm by providing a satisfactory prediction on 303 patient’s data.