Cardiovascular Disease (CVD) Prediction using Machine Learning Algorithms

This paper deals with the prediction of Cardiovascular Disease (CVD) by performing an analysis of six supervised machine learning algorithms such as K-Nearest Neighbors Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Machine Classifier and Linear Discriminant Analysis. Further, by introducing two health risk factors (feature extraction) - Blood Pressure and Body Mass Index - into the dataset, we have observed an increase in the accuracy. Feature selection was performed to find out the important risk factors. Our main goal is to find the most optimal results in terms of CVD prediction from the available dataset.