Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques DOI Creative Commons

Lorena Balliu,

Blerina Zanaj,

Gledis Basha

et al.

Serbian Journal of Electrical Engineering, Journal Year: 2024, Volume and Issue: 21(3), P. 375 - 390

Published: Jan. 1, 2024

ML (Machine Learning) is frequently used in health systems to alert physicians real time. This helps take preventive measures, such as predicting a future heart attack. study presents combined with various forms of feature selection identify disease. It includes the analysis different algorithms Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and hybrid models. results SVM RM performing better after applying for individual Meanwhile, cases provide good if ensemble done using Voting Classifier. Our approach this paper based on our existing literature methodologies. We can conclude that, dataset, Classifier appears be most accurate precise model out all classifiers that use techniques.

Language: Английский

Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques DOI Creative Commons

Lorena Balliu,

Blerina Zanaj,

Gledis Basha

et al.

Serbian Journal of Electrical Engineering, Journal Year: 2024, Volume and Issue: 21(3), P. 375 - 390

Published: Jan. 1, 2024

ML (Machine Learning) is frequently used in health systems to alert physicians real time. This helps take preventive measures, such as predicting a future heart attack. study presents combined with various forms of feature selection identify disease. It includes the analysis different algorithms Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and hybrid models. results SVM RM performing better after applying for individual Meanwhile, cases provide good if ensemble done using Voting Classifier. Our approach this paper based on our existing literature methodologies. We can conclude that, dataset, Classifier appears be most accurate precise model out all classifiers that use techniques.

Language: Английский

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