Prediction of Heart Disease Using Machine Learning for Framingham Dataset

Cardiovascular Diseases (CVDs) are considered a silent killer as many people fall prey in the hand of death without any prior warning. This work represents various attributes, habits, parameters related to human beings and their connection to heart disease. Machine learning model based algorithms such as Naive Bayes, Decision Tree, K-nearest Neighbor, Logistic Regression, Recurrent Neural Network, Random Forest algorithm have been evaluated for Cardiovascular Disease (CVD) prediction. It was found that among all the algorithms evaluated, logistic regression performs best in predicting cardiovascular disease.