Heart Disease Detection Using ML

Abstract

Hearth disease is one of the leading causes of death globally and a common disease in the middle and old ages. Among all heart diseases, heart attack and strokes are the most common cardiac illness that is the responsible majority of heart disease death. To identify heart diseases, for instance, Angiography is costly and has significant side effects. Therefore, machine learning can play an important role in identifying and predicting the potential risk factor of cardiac disease based on clinical and patient data, which is affordable and reliable. This study proposed and evaluated six machine learning models using survey data of 400k US residents to predict heart disease. This study also compared the evaluated six machine learning models, which are Xgboost, Bagging, Random Forest, Decision Tree, K-Nearest Neighbor, and Naïve Bayes. The accuracy, sensitivity, F1-score, and AUC of six machine learning algorithms are also evaluated and presented. In terms of performance results, the Xgboost model showed optimized results with an accuracy rate of 91.30%.