Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
A Comparative Study: Prediction of Heart Disease During Stress Testing Device Based on Machine Learning and Artificial Techniques
Faris S. H. Azba, Omar AlSaif, Mazin N. Farhan
23 January 2026 · Vol. 2, Issue 1, pp. 19-24
DOI: 10.65150/EP-gjetr/V2E1/2026-04
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What is this article about?
The heart is the most important organ in the human body. Given the dynamic nature of cardiac function under stress, predicting heart failure during physical exertion is a major challenge in clinical practice.
Article at a glance
- Published
- 23 January 2026
- Volume / issue / pages
- Vol. 2, Issue 1, pp. 19-24
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
The heart is the most important organ in the human body. Given the dynamic nature of cardiac function under stress, predicting heart failure during physical exertion is a major challenge in clinical practice. Machine learning (ML) provides a transformative approach for analyzing complex physiological and clinical data and achieving accurate and timely predictions. This study aims to review the findings of previous studies on the use of AI in heart disease diagnosis. Several machine learning algorithms, including random forests, support vector machines (SVMs), deep neural networks (DNNs), and others, were evaluated to determine their predictive performance. The results showed that deep neural networks outperformed other models, achieving 92% accuracy and 94% sensitivity. Their ability to adapt to generally nonlinear patterns also demonstrate the importance of some features over others, such as cholesterol levels, blood pressure, chest pain, and heart rate. The study concludes by highlighting the importance of AI in supporting and assisting medical professionals, particularly in the field of heart health, which can advance beyond diagnosis and prediction.
Keywords: Heart Failure Prediction, machine learning, DNN, SVM, Heart Stress.
Cite this article
Faris S. H. Azba, Omar AlSaif, & Mazin N. Farhan (2026). A Comparative Study: Prediction of Heart Disease During Stress Testing Device Based on Machine Learning and Artificial Techniques. Global Journal of Engineering and Technology Research, 2(1), 19-24. https://doi.org/10.65150/EP-gjetr/V2E1/2026-04
@article{Faris2026,
title = {A Comparative Study: Prediction of Heart Disease During Stress Testing Device Based on Machine Learning and Artificial Techniques},
author = {Faris S. H. Azba and Omar AlSaif and Mazin N. Farhan},
journal = {Global Journal of Engineering and Technology Research},
year = {2026},
volume = {2},
number = {1},
pages = {19-24},
doi = {10.65150/EP-gjetr/V2E1/2026-04},
url = {https://doi.org/10.65150/EP-gjetr/V2E1/2026-04}
}Questions about this article
Who wrote this article?
Faris S. H. Azba, Omar AlSaif, Mazin N. Farhan.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 2, Issue 1, pages 19-24, published by EP Journals Group on 23 January 2026.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V2E1/2026-04 (https://doi.org/10.65150/EP-gjetr/V2E1/2026-04).
How do I cite this article?
APA: Faris S. H. Azba, Omar AlSaif, & Mazin N. Farhan (2026). A Comparative Study: Prediction of Heart Disease During Stress Testing Device Based on Machine Learning and Artificial Techniques. Global Journal of Engineering and Technology Research, 2(1), 19-24. https://doi.org/10.65150/EP-gjetr/V2E1/2026-04
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