Performance analysis of machine learning algorithms in heart disease prediction

Abstract

This work presents performance analysis of machine learning algorithms such as logistic regression, naive bayes, decision tree, k nearest neighbour, random forest, support vector machine, and extreme gradient boosting in heart disease prediction. Machine learning algorithms are implemented in python using Scikit learn library in Jupiter notebook. Experiments are conducted by training and testing machine learning algorithms using kaggle heart disease dataset under six test cases. Performance of machine learning algorithms are evaluated using accuracy, precision, recall, F1 score and ROC as metrics. Results show random forest reported high accuracy, precision, recall, F1 score and ROC in heart disease prediction compared to other machine learning algorithms in all six test cases. Results show RF is effective in heart disease prediction in Case 3 with 80% train data and 20% test data.