Prediction of Cardiac Disease using Supervised Machine Learning Algorithms

Abstract

The healthcare industry is dealing with billions of patients all over the world and producing massive data. The machine learning-based models are dissecting the multidimensional medical datasets and generating better insights. In this study, a cardiovascular dataset is classified by using several state-of-the-art Supervised Machine Learning algorithms that are precisely used for disease prediction. The results indicate that the Decision Tree classification model predicted the cardiovascular diseases better than Naive Bayes, Logistic Regression, Random Forest, SVM and KNN based approaches. The Decision Tree bequeathed the best result with the accuracy of 73%. This approach could be helpful for doctors to predict the occurrence of heart diseases in advance and provide appropriate treatment.