Evaluation of Machine Learning Algorithms for the Detection of Cardiovascular Diseases

Abstract

Cardiovascular or heart diseases are generally referred to conditions which affect the heart and its blood vessels in a bad way. To predict them accurately and timely is a challenging task. Machine learning is useful in making predictions and decisions from the large quantity of data produced. This research paper aims to do a comparative study for Heart Disease Prediction using Machine Learning algorithms like Logistic Regression, Support Vector Machine (SVM), Naive Bayes, , k-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Xgboost and evaluate their outputs using performance metrics.