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Deep Learning Prediction Model for Heart Disease for Elderly Patients

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud*

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Hanan Ahmed Hosni Mahmoud. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2527-2540.


The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with a full and minimum set of features. Fewer features enhance the processing time of the classification process while the accuracy is compromised. The performance of classifiers with less features has been analyzed with experimental results. The proposed system is built on the Internet of Things Platform for medical data for the classification process which aids medical professionals to detect heart diseases through cloud platforms. The results accuracy is matched to classical learning models such as Convolutional Neural Network (CNN), Deep CNN, and neural ensemble models. The analysis of the proposed diagnostic system can determine the heart disease risks efficiently. Experimental results demonstrate that flexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.


Cite This Article

A. A. AlArfaj and H. A. H. Mahmoud, "Deep learning prediction model for heart disease for elderly patients," Intelligent Automation & Soft Computing, vol. 35, no.2, pp. 2527–2540, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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