Citation

Raza, Hussain and Al-Helali, Marwah Zaid Mohammed and Attaullah, Hafiz Muhammad and Ibrahim, Thabit Mahmood (2026) Deep Architectural Classification for Heart Disease Prediction. Information Systems Engineering and Management, 86. pp. 35-49. ISSN 3004-958X

Text Deep Architectural Classification for Heart Disease Prediction _ Springer Nature Link.pdf - Published Version Restricted to Repository staff only Download (778kB)

Abstract

Machine Learning (ML) algorithms have gained popularity in heart disease prediction, with numerous applications such as predicting heart attacks and heart failure. Several well-known ML algorithms were used for comparison, including Random Forest, Naïve Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbor (KNN), XGBoost and Support Vector Machine (SVM) models. Integrating ML into the healthcare sector offers a sense of intelligent security in diagnosis and prognosis. To validate the predictions, the same level of comparison with identical parameters was applied across three famous datasets: BRFSS2015, the Hungarian Institute of Cardiology datase, and the University Hospital Zurich dataset. Interestingly, the consistency of results across these datasets highlights the novelty of this approach. The project’s GitHub link is: https://github.com/SHussainRR/HeartDisease-ML-ComparisonAnalysis

Item Type:	Article
Uncontrolled Keywords:	Machine learning, Random Forest, SVM
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Oct 2026 08:52
Last Modified:	01 Oct 2026 08:52
URII:	http://shdl.mmu.edu.my/id/eprint/16805

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