Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques - Balliu Lorena; Zanaj Blerina; Basha Gledis; Zanaj Elma; Meçe Elinda Kajo (Serbian Journal of Electrical Engineering, 2024)
ML (Machine Learning) is frequently used in health systems to alert physicians in real time. This helps to take preventive measures, such as predicting a future heart attack. This study presents ML combined with various forms of feature selection to identify heart disease. It includes the analysis of different algorithms such as Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and hybrid models. This results in SVM and RM performing better after applying feature selection for individual ML models. Meanwhile, hybrid cases provide good results if the ensemble is done using a Voting Classifier. Our approach in this paper is based on our study of existing literature and methodologies. We can conclude that, for the used dataset, the Voting Classifier appears to be the most accurate and precise model out of all individual and hybrid classifiers that use feature selection techniques. This article has been corrected. Link to the correction 10.2298/SJEE2501145E