Exploring important factors in predicting heart disease based on ensembleextra feature selection approach

Howida Abubaker and Farkhana Muchtar and Alif Ridzuan Khairuddin and Ahmad Najmi Amerhaider Nuar and Zuriahati Mohd Yunos and Carolyn Salimun (2024) Exploring important factors in predicting heart disease based on ensembleextra feature selection approach. Baghdad Science Journal, 21. pp. 1-20. ISSN 2078-8665

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Abstract

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficacy of several classification algorithms on four reputable datasets, using both the full features set and the reduced features subset selected through the proposed method. The results show that the feature selection technique achieves outstanding classification accuracy, precision, and recall, with an impressive 97% accuracy when used with the Extra Tree classifier algorithm. The research reveals the promising potential of the feature selection method for improving classifier accuracy by focusing on the most informative features and simultaneously decreasing computational burden.

Item Type:	Article
Keyword:	Extra Tree, Feature selection, Feature subsets, Heart Disease Dataset, Machine learning
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	21 Apr 2025 11:50
Last Modified:	21 Apr 2025 11:50
URI:	https://eprints.ums.edu.my/id/eprint/43547

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