AI-Powered ECG Prediction System for Detecting Cardiovascular Disease

Year : 2025 | Volume : 15 | Issue : 03 | Page : 51 85
    By

    Mohammad N. Alam,

  • Vijay Laxmi,

  • Baljinder Kaur,

  1. Research Scholar, Department of CSE, Guru Kashi University, Bathinda, Punjab, India
  2. Assistant Professor, Department of Computer Applications, Guru Kashi University, Bathinda, Punjab, India
  3. Assistant Professor, Department of Computer Applications, Guru Kashi University, Bathinda, Punjab, India

Abstract

The proposed AI-powered CardioSmart Analyzer, an electrocardiogram (ECG) prediction system, presents an innovative and scientifically rigorous approach to the real-time automated analysis of ECG signals for diagnosing various heart conditions. This research focused on building a predictive model to identify cardiovascular diseases (CVD) using ECG data. A dataset comprising 2,840 12-lead ECG recordings was gathered from medical facilities in Gazipur, Bangladesh, over the period from June to August 2024. The analysis revealed 68 unique diagnostic categories based on the interpretations of the patients’ ECG reports. To carry out the classification and prediction of these cardiovascular conditions, a robust random forest algorithm was implemented. This machine learning model proved to be highly effective, yielding outstanding performance results. In both binary and multi-class classification tasks, the algorithm achieved a remarkable accuracy rate of 100%. The success of this approach highlights its potential application in clinical settings, where automated ECG interpretation could assist healthcare professionals in the early and accurate diagnosis of a wide range of heart-related conditions. Overall, AI-driven ECG-based prediction model exhibited excellent performance in detecting common CVD conditions.

Keywords: ECG prediction, random forest, early detection, CVD condition, interpretation

[This article belongs to Research and Reviews: A Journal of Health Professions ]

How to cite this article:
Mohammad N. Alam, Vijay Laxmi, Baljinder Kaur. AI-Powered ECG Prediction System for Detecting Cardiovascular Disease. Research and Reviews: A Journal of Health Professions. 2025; 15(03):51-85.
How to cite this URL:
Mohammad N. Alam, Vijay Laxmi, Baljinder Kaur. AI-Powered ECG Prediction System for Detecting Cardiovascular Disease. Research and Reviews: A Journal of Health Professions. 2025; 15(03):51-85. Available from: https://journals.stmjournals.com/rrjohp/article=2025/view=230499


References

  1. Sumalatha U, Prakasha KK, Prabhu S, Nayak VC. Deep learning applications in ECG analysis and disease detection: An investigation study of recent advances. IEEE Access. 2024;12. doi: 10.1109/ACCESS.2024.3447096.
  2. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32(3):230–236. doi: 10.1109/TBME.1985.325532.
  3. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):e215–e220. doi: 10.1161/01.cir.101.23.e215.
  4. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi: 10.1023/A:1010933404324.
  5. Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci. 2017;405(C):81–90. doi: 10.1016/j.ins.2017.04.012.
  6. Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 2001;20(3):45–50. doi: 10.1109/51.932724.
  7. Kalmady SV, Salimi A, Sun W, Sepehrvand N, Nademi Y, Bainey K, et al. Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level. NPJ Digit Med. 2024;7(1):133. doi: 10.1038/s41746-024-01130-8.
  8. Aziz S, Ahmed S, Alouini MS. ECG-based machine-learning algorithms for heartbeat classification. Sci Rep. 2021;11(1):18738. doi: 10.1038/s41598-021-97118-5.
  9. Pham H, Egorov K, Kazakov A, Budennyy S. Machine learning-based detection of cardiovascular disease using ECG signals: Performance vs. complexity. Front Cardiovasc Med. 2023;10:1229743. doi: 10.3389/fcvm.2023.1229743.
  10. Ayano YM, Schwenker F, Dufera BD, Debelee TG. Interpretable machine learning techniques in ECG-based heart disease classification: A systematic review. Diagnostics (Basel). 2022;13(1):111. doi: 10.3390/diagnostics13010111.
  11. Abubaker MB, Babayiğit B. Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods. IEEE Trans Artif Intell. 2022;4(2):373–382. doi: 10.1109/TAI.2022.3159505.
  12. Gondane R, Rajhans R, Susheela Devi V. Classification using probabilistic random forest. In: 2015 IEEE Symposium Series on Computational Intelligence. Piscataway: IEEE; 2015. pp. 174–179. doi: 10.1109/SSCI.2015.35.
  13. Abubaker MB, Babayiğit B. Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods. IEEE Trans Artif Intell. 2023;4(2):373–82. doi:10.1109/TAI.2022.3159505.
  14. Venkatesan P, Karthigaikumar S, Paul A, Satheeskumaran S, Kumar R. ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access. 2018;6:9767–9773. doi: 10.1109/ACCESS.2018.2794346.
  15. Bazi Y, Alajlan N, AlHichri H, Malek S. Domain adaptation methods for ECG classification. In: 2013 IEEE International Conference on Computer Medical Applications (ICCMA). Piscataway: IEEE; 2013. pp. 1–4. doi: 10.1109/ICCMA.2013.6506156.
  16. Pachiyannan M, Alsulami D, Alsadie AKJ, Saudagar M, AlKhathami M, Poonia RC. A novel machine learning-based prediction method for early detection and diagnosis of congenital heart disease using ECG signal processing. Technologies. 2024;12(1):4. doi: 10.3390/technologies12010004.
  17. Maleki M, Haeri F. Identification of cardiovascular diseases through ECG classification using wavelet transformation. arXiv [Preprint]. 2024. doi: 10.48550/arXiv.2404.09393.
  18. Mincholé A, Camps J, Lyon A, Rodríguez B. Machine learning in the electrocardiogram. J Electrocardiol. 2019;57S:S61–S64. doi: 10.1016/j.jelectrocard.2019.08.008.
  19. De S, Chakraborty B. Disease Detection System (DDS) using machine learning technique. In: Dey N, Bhateja V, Hassanien AE, editors. International Conference on Machine Learning with Health Care Perspective. Singapore: Springer; 2020. pp. 107–132.
  20. Fatima M, Pasha M. Survey of machine learning algorithms for disease diagnostic. J Intell Learn Syst Appl. 2017;9(1):1–16. doi: 10.4236/jilsa.2017.91001.
  21. Richens JG, Lee CM, Johri S. Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun. 2020;11(1):3923. doi: 10.1038/s41467-020-17419-7.
  22. Babu MG, Shankar G. An exploration of ECG signal feature selection and classification using machine learning techniques. Int J Innov Technol Explor Eng. 2020;9(3):1917–1922. doi: 10.35940/ijitee.C8728.019320.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 27/05/2025
Accepted 24/06/2025
Published 04/11/2025
Publication Time 161 Days
158

Login


My IP

PlumX Metrics