Early and Accurate Prediction of Heart Disease Using Machine Learning Model

Abstract

Heart disease is one of the critical health issues and many people across the world are suffering with this disease. It is important to identify this disease in early stages to save many lives. The purpose of this article is to design a model to predict the heart diseases using machine learning techniques. This model is developed using classification algorithms, as they play important role in prediction. The model is developed using different classification algorithms which include Logistic Regression, Random Forest, Support vector machine, Gaussian Naïve Bayes, Gradient boosting, K-nearest neighbours, Multinomial Naïve bayes and Decision trees. Cleveland data repository is used to train and test the classifiers. In addition to this, feature selection algorithm named chi square is used to select key features from the input data set, which will decrease the execution time and increases the performance of the classifiers. Out of all the classifiers evaluated using performance metrics, Random forest is giving good accuracy. So, the model built using Random forest is efficient and feasible solution in identifying heart diseases and it can be implemented in healthcare which plays key role in the stream of cardiology.