Heart Disease Prediction using Synthetic Minority Oversampling Technique and Soft Voting

Abstract

Heart disease is a vital cause of mortality in this world. The number of patients with this noxious disease is rising every day. It is taking millions of lives each year. It is dismaying that there are not many effective ways to detect heart disease gleaned on elementary information. Nowadays, in order to achieve unprecedented results, Machine Learning (ML) has been exclusively used in various fields. So, we have come up with a proposition of a heart disease prediction model using ML techniques in this paper to accomplish an effective result. We have used different ML classifiers such as Gaussian Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and applied Soft Voting on them. The result shows that the Voting methods give us the most effective results with an Accuracy of 92.42%, Precision of 92.50%, Recall of 92.22% and F1-score of 92.34%. Our purpose is to detect this deleterious disease more precisely to enhance the medical field.