Citation

Alobaedy, Mustafa Muwafak and Wang, Xiaolin and Chamran, M. Kazem and Hafiz Ibrahim, Mohd Nurul (2025) Performance Benchmarking of Classic Classification Algorithms on Structured Heart Disease Data. In: 2025 IEEE 15th International Conference on System Engineering and Technology (ICSET), 04-04 October 2025, Kuala Lumpur, Malaysia.

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Abstract

Cardiovascular disease is one of the major causes of death in the world, and it is important to achieve early prediction with the help of machine learning. In this paper, four algorithms, namely, K-nearest neighbors (KNN), Support Vector Machine (SVM), decision tree and random forest, are selected to construct prediction models based on structured heart disease dataset and perform comparative performance analysis. Through standardized processing, feature screening and SMOTE oversampling, the data quality and model generalization ability are improved. Experiments show that Random Forest performs best in terms of accuracy (92.48 %) and AUC (0.9731), and KNN has the highest recall (0.95), which is suitable for high sensitivity tasks. Combined with 19 related literatures, this paper validates the suitability of each model in medical scenarios and suggests differentiated applications. This study provides theoretical basis and empirical support for the selection and deployment of intelligent prediction models for heart disease.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Heart Disease Prediction, Machine Learning, Model Comparison, Random Forest, Medical AI
Subjects:	R Medicine > RZ Other systems of medicine
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	17 Mar 2026 07:24
Last Modified:	19 Mar 2026 00:04
URII:	http://shdl.mmu.edu.my/id/eprint/15515

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