Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method

This paper presents a majority voting ensemble method that is able to predict the possible presence of heart disease in humans. The prediction is based on simple affordable medical tests conducted in any local clinic. Moreover., the aim of this project is to provide more confidence and accuracy to the Doctor's diagnosis since the model is trained using real-life data of healthy and ill patients. The model classifies the patient based on the majority vote of several machine learning models in order to provide more accurate solutions than having only one model. Finally, this approach produced an accuracy of 90% based on the hard voting ensemble model.