Machine Learning Models for Prediction of Heart Disease

World Health Organization reported that heart diseases are the prominent cause of casualty and also increase year by year. Timely treatment increases the possibility of cure. For this, earlier prediction and accurate diagnosis are essential. Because of today’s technological advancements, prediction with more accuracy and precision is possible. Machine learning (ML) algorithms attract the attention of researchers in prediction modelling due to the accuracy, precision, and reliability of prediction. Hence, in this study an attempt is made to predict the heart diseases using ML algorithms for instance, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). For the study, the datasets of Cleveland, Switzerland, Hungarian, Long Beach VA, and Stalog (Heart) are considered and the prediction is done by using the aforementioned ML algorithms. Finally, the results are compared, and concluded that RF and SVM produce better prediction results than others.