A Comparative Study: Prediction of Heart Disease During Stress Testing Device Based on Machine Learning and Artificial Techniques
DOI:
https://doi.org/10.65150/EP-gjetr/V2E1/2026-04Keywords:
Heart Failure Prediction,machine learning, DNN,SVM, Heart Stress.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.
References
1) Yancy Yancy, et al. "guideline for the management of heart failure," Journal of the American College of Cardiology, 1495-1539, 2013.
2) Ponikowski, P., et al., "Heart failure: epidemiology and prevention," ESC Heart Failure, 2020.
3) Shah, S. J., et al., "Machine learning assessment of cardiac diastolic function," Journal of the American College of Cardiology, 2020.
4) Attia, Z. I., et al., "Screening for cardiac contractile dysfunction using AI," Nature Medicine, 2019.
5) Chicco, D., et al., "Machine learning and heart failure diagnosis," BMC Medical Informatics and Decision Making, 2020.
6) Kwon, J. M., et al., "AI algorithms for cardiac arrest prediction," Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 2019.
7) Weng, S. F., et al., "Can machine learning improve cardiovascular risk prediction?" PLOS ONE, 2017.
8) Choi, E., et al., "Doctor AI: Predicting clinical events via RNNs," Journal of Medical Informatics, 2016.
9) Bandyopadhyay, S., et al., "Predicting heart failure using machine learning techniques," Journal of Healthcare Engineering, 2019.
10) Al-Mallah, M. H., et al., "Predicting heart failure readmissions using ML," IEEE Access, 2019.
11) Oktay, O., et al., "Anatomically constrained neural networks for cardiac segmentation," IEEE Transactions on Medical Imaging, 2017.
12) Attia, Z. I., et al., "Artificial intelligence-enabled ECG for atrial fibrillation detection," The Lancet, 2019.
13) K. Vembandasamy, R. Sasipriya, and E. Deepa, "Heart diseases detection using Naive Bayes algorithm," International Journal of Innovative Science, Engineering & Technology (IJISET), vol. 2, no. 9, pp. 441–446, Sep. 2015
14) E. Choi et al., "Doctor AI: Predicting clinical events via recurrent neural networks," Machine Learning for Healthcare Conference, pp. 301-318, 2016.
15) O. Oktay et al., "Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation," IEEE Transactions on Medical Imaging, vol. 37, no. 2, pp. 384-395, 2017.
16) E. Choi et al., "Using recurrent neural network models for early detection of heart failure onset," Journal of the American Medical Informatics Association, vol. 24, no. 2, pp. 361-370, 2017.
17) S. F. Weng et al., "Can machine-learning improve cardiovascular risk prediction using routine clinical data?" PLOS ONE, vol. 12, no. 4 , 2017.
18) M. H. Al-Mallah et al., "Predicting heart failure readmissions: machine learning applied to electronic health records," Journal of the American College of Cardiology, vol. 73, no. 9, pp. 1079-1089, 2019.
19) M. S. Amin et al., "Predicting heart failure readmissions using machine learning techniques," IEEE Access, vol. 7, pp. 16016-16023, 2019.
20) Z. I. Attia et al., "Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram," Nature Medicine, vol. 25, no. 1, pp. 70-74, 2019
21) S. R. Tithi, A. Aktar, F. Aleem, and A. Chakrabarty, “ECG data analysis and heart disease prediction using machine learning algorithms,” in Proceedings of the 2019 International Conference on Machine Learning and Data Science (MLDS), New Delhi, India, 2019, pp. 123–130.
22) D. Chicco and G. Jurman, "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone," BMC Medical Informatics and Decision Making, vol. 20, no. 1, pp. 16, 2020.
23) O. Akbilgic, L. Butler, I. Karabayir, P. P. Chang, D. W. Kitzman, A. Alonso, L. Y. Chen, and E. Z. Soliman, "ECG-AI: Electrocardiographic artificial intelligence model for prediction of heart failure," Eur. Heart J. - Digital Health, vol. 2, pp. 626–634, 2021.
24) Ahmed S. Fahmy, Ethan J. Rowin, Warren J. Manning, Martin S. Maron, and Reza Nezafat, "Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy," Frontiers in Cardiovascular Medicine, vol. 8, Heart Failure and Transplantation section, May 2021
25) Y. Haque, R. S. Zawad, C. S. A. Rony, H. Al Banna, T. Ghosh, M. S. Kaiser, and M. Mahmud, “State-of-the-art of stress prediction from heart rate variability using artificial intelligence,” Cognitive Computation, vol. 14, no. 5, pp. 1557–1576, 2022
26) A. Rath, D. Mishra, G. Panda, and S. C. Satapathy, “Heart disease detection using deep learning methods from imbalanced ECG samples,” Biomedical Signal Processing and Control, vol. 72, pp. 1–10, Mar. 2022.
27) Zhoujian Sun, Wei Dong, Hanrui Shi, Hong Ma, Lechao Cheng, and Zhengxing Huang, "Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis," Frontiers in Cardiovascular Medicine, vol. 9, 2022.
28) Sangeeta Singhal, "Predicting Congestive Heart Failure Using Predictive Analytics in AI," INTERNATIONAL JOURNAL CREATIVE RESEARCH IN COMPUTER TECHNOLOGY AND DESIGN), vol. 5, no. 5, 2023.
29) D. H. Barzilai, E. Zimlichman, M. Cohen-Shelly, V. Sorin, E. Massalha, T. G. Allison, and E. Klang, "Machine learning in cardiac stress test interpretation: a systematic review," Eur. Heart J. - Digital Health, vol. 5, pp. 401–408, 2024,
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Faris S. H. Azba , Omar AlSaif , Mazin N. Farhan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
