Enhancing Cardiovascular Disease Prediction Using Hard Voting Technique in Machine Learning

Abstract

Cardio Vascular Disease (CVD) or Heart disease is one of the leading causes of death around the globe. Early identification of the disease can significantly save precious lives. But the identification of heart-related diseases is a challenging task as it relies on a wide range of factors. Machine Learning algorithms have strong potential in prediction-related domains. In this paper, we have used an Ensembled model called the Hard Vot-ing Ensemble Model to detect heart disease. A dataset containing 13 features is taken from the UCI repo using Kaggle. Seven different algorithms are used, tested, and trained, accuracy is measured and out of those, models with the best accuracy are picked and ensembled together. The ensemble model resulted in higher accuracy than all other individual models.