A Systematic Review of Machine Learning, Deep Learning, and Explainable AI Approaches for Cardiac Disease Prediction
Article Sidebar
Main Article Content
Abstract
The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep learning (DL) and hybrid algorithms of artificial intelligence to predict heart diseases has enjoyed a widespread use in the recent years with different levels of success. The current paper provides a systematized review of the latest ML-, DL-, and hybrid-based systems to predict heart disease that include ensemble model, deep learning model, explainable artificial intelligence (XAI), and privacy-preserving model. The analyzed studies are evaluated based on datasets, classifiers, validation methods, data balancing and evaluation measures methods. It is illustrated in the analysis that the traditional ML models are most likely to have the prediction accuracy in between 80-90, the ensemble and hybrid models will most likely have the prediction accuracy in between 90-98. Recent explainable and fine-tuned ensemble methods show an accuracy of a level of 99, however, frequently under controlled conditions. Irrespective of the developments made, the issues include low interpretability, excessive benchmark data, imbalance in classes, and computational complexity. According to the research gaps that are mentioned, the given review reflects the necessity of clarifiable hybrid models that include high performing ensemble models like XGBoost and Deep learning-based feature fusion to increase the predictive quality, transparency and clinical usability.
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
References
World Health Organization, Cardiovascular diseases (CVDs), Geneva, Switzerland, 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Budihal and Kawale, “A systematic review of machine learning models for cardiac disease prediction,” International Journal of Computer Applications, vol. 186, no. 63, pp. 27–32, Jan. 2025, ISSN: 0975-8887.
K. H. Miao, J. H. Miao, and G. J. Miao, “Diagnosing coronary heart disease using deep neural networks,” IEEE Access, vol. 6, pp. 66095–66102, 2018.
A. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, “Heart disease dataset,” UCI Machine Learning Repository, Univ. of California, Irvine, 1988. [Online]. Available: https://archive.ics.uci.edu/dataset/45/heart+disease.
The Devastator, “Predicting heart disease risk using clinical variables,” Kaggle Dataset, 2025. [Online]. Available: https://www.kaggle.com/datasets/thedevastator/predicting-heart-disease-risk-using-clinical-var. Accessed: 2025.
N. Biswas et al., “Machine learning-based model to predict heart disease in early stage employing different feature selection techniques,” BioMed Res. Int., vol. 2023, Art. no. 6864343, pp. 1–14, 2023, doi: 10.1155/2023/6864343.
S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019.
P. Shah, M. Shukla, N. H. Dholakia, and H. Gupta, “Predicting cardiovascular risk with hybrid ensemble learning and explainable AI,” Scientific Reports, vol. 15, Art. no. 17927, 2025, doi: 10.1038/s41598-025-01650-7.
U. Nagavelli, S. Samanta, and P. Chakraborty, “Machine learning technology-based heart disease detection models,” J. Healthcare Eng., vol. 2022, 2022.
S. Subramani, R. Balakrishnan, and V. Ramalingam, “Cardiovascular disease prediction using machine learning and deep learning techniques,” Frontiers in Medicine, vol. 10, Article ID 1150933, 2023.
C. Zhou, P. Dai, A. Hou, Z. Zhang, and F. Wang, “A comprehensive review of deep learning-based models for heart disease prediction,” Artif. Intell. Rev., vol. 57, Article ID 263, 2024.
G. Holste, E. K. Oikonomou, B. J. Mortazavi, Z. Wang, and R. Khera, “Efficient deep learning–based automated diagnosis from echocardiography using contrastive self-supervised learning,” Communications Medicine, vol. 4, Article ID 133, 2024.
H. El-Sofany, B. Bouallegue, and Y. M. Abd El-Latif, “A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method,” Scientific Reports, vol. 14, Article ID 23277, 2024.
R. Wajgi, S. Patil, and P. Kulkarni, “Heart disease prediction using graph neural networks,” IEEE Access, vol. 12, pp. 55421–55433, 2024.
M. Hamid, A. Rahman, and M. Islam, “Fine-tuned CatBoost machine learning approach for early detection of cardiovascular disease,” IEEE Access, vol. 13, pp. 99876–99888, 2025.
M. Qadeer, H. Ali, and S. Khan, “Heart failure prediction through comparative study of machine learning and deep learning models,” Sensors, vol. 25, no. 3, Article ID 456, 2025.
D. Teja and G. M. Rayalu, “Optimizing heart disease diagnosis with advanced machine learning models: a comparison of predictive performance,” BMC Cardiovascular Disorders, vol. 25, Article ID 212, 2025.
Budihal et. al.: “Heart Disease Prediction Using Machine Learning On A Real-Time Clinical Dataset With Multi-Diagnostic Features”. International Journal of Applied Mathematics. Vol. 38 No. 12s (2025) https://doi.org/10.12732/ijam.v38i12s.1584
D. Rohan, G. P. Reddy, Y. V. P. Kumar, K. P. Prakash, and C. P. Reddy, “An extensive experimental analysis for heart disease prediction using artificial intelligence techniques,” Scientific Reports, vol. 15, Article ID 6132, 2025.
Y. Rimal, N. Sharma, S. Paudel, A. Alsadoon, and S. Gill, “Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy,” Scientific Reports, vol. 15, Article ID 13444, 2025.
S. Al Gharib, J. Charafeddine, F. Dornaika, and S. Haddad, “Hybrid learning framework for explainable cardiovascular disease detection,” IEEE Access, vol. 13, pp. 130168–130182, 2025, doi: 10.1109/ACCESS.2025.3591241.
Navita, P. Mittal, Y. K. Sharma, U. K. Lilhore, S. Simaiya, and K. Saleem, “Advanced hybrid machine learning model for accurate detection of cardiovascular disease,” Int. J. Comput. Intell. Syst., vol. 18, Article ID 51, 2025.
M. Tyagi, N. Jain, and S. Verma, “Heart disease detection using federated learning,” IEEE J. Biomed. Health Inform., vol. 29, no. 4, pp. 2345–2356, 2025.
C. Gnanavelu et al., “Cardiovascular disease prediction using machine learning performance metrics,” J. Young Pharm., vol. 17, no. 1, pp. 226–233, 2025, doi: 10.5530/jyp.20251231.
S. Mall, “Heart attack prediction using machine learning techniques,” in Proc. 4th Int. Conf. Advance Computing and Innovative Technologies in Engineering (ICACITE), Ghaziabad, India, 2024, pp. 1778–1783, doi: 10.1109/ICACITE60783.2024.10617300.