Citation

Hussainy, F. Syed Anwar and Jayapradha, J. and Roslee, Mardeni and Kumar, T. Senthil and Sudhamani, Chilakala and Mitani, Sufian Mousa Ibrahim and Osman, Anwar Faizd and Ali, Fatimah Zaharah (2025) Improved Interpretation Model for Heart Disease Diagnosis Using Artificial Neural Networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 16 (2). pp. 136-153. ISSN 20935374

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Abstract

In the present scenario, health care is a predestined process to be considered in human life. While heart diseases are concerned, cardiovascular disease (CVD) is a wide class of diseases that damages blood vessels and the heart. In the medical field, huge health data are available to study and process; hence, machine learning methods are required for appropriate decision-making, specifically in terms of heart disease prediction and diagnosis. For enhancing the appropriation rate of decision-making in CVD diagnosis, this paper proposes an Improved Interpretation Model for Heart Disease Diagnosis (IIM-HDD) using Artificial neural networks. The model incorporates data acquisition, pre-processing, feature selection, training, and testing for diagnosis. For training and validation, the data from benchmark datasets are combined and used. Moreover, feature selection is computed with a relief-based selection process. The ANN model is trained to produce the output, corresponding to their input features. The results computations are processed with metrics that include classification, accuracy, precision rate, error rate, specificity, sensitivity, F1 score, and other comparisons are also provided for proving the proposed model. The results show that the work outperforms the other compared models in respective metrics.

Item Type:	Article
Uncontrolled Keywords:	Artificial Neural Network, Cardiovascular Disease, Classification Accuracy, Disease Diagnosis, Feature Selection.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Aug 2025 05:01
Last Modified:	27 Aug 2025 05:01
URII:	http://shdl.mmu.edu.my/id/eprint/14460

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