An Explainable Hybrid Ensemble-Based Analytical Framework for Interpretable and Early Prediction of Coronary Artery Disease
Keywords:
Coronary Artery Disease, Explainable Artificial Intelligence, Ensemble Learning, Machine Learning in Healthcare, Clinical Decision Support Systems
Abstract
Abstract. Coronary Artery Disease (CAD) is among the leading causes
of mortality worldwide. Early detection systems are essential for improving
preventive cardiology and reducing cardiovascular risk. This research
proposes an explainable hybrid ensemble-based analytical framework designed
for early and interpretable CAD prediction.
The proposed system integrates heterogeneous machine learning classifiers
including Logistic Regression, Random Forest, Gradient Boosting,
and Support Vector Machines through a stacking-based ensemble approach.
An explainability layer based on feature attribution techniques
is incorporated to provide both global and instance-level interpretation
of predictions.
Experimental analysis demonstrates that the proposed framework achieves
improved predictive accuracy and sensitivity compared with traditional
statistical models and standalone machine learning algorithms. Explainability
results highlight clinically significant risk factors such as age,
cholesterol levels, blood pressure, smoking status, and glucose levels. The
framework supports physician-assisted decision-making rather than automated
diagnosis, enabling responsible AI adoption in healthcare.
References
1. Alizadehsani, R., Roshanzamir, M., Hussain, S., et al.: Coronary artery disease
detection using machine learning techniques: A review. Computers in Biology and
Medicine 137, 104654 (2022)
2. Arnett, D.K., Blumenthal, R.S., Albert, M.A., et al.: 2019 acc/aha guideline on the
primary prevention of cardiovascular disease. Circulation 143(8), e78–e91 (2021)
3. Ather, D., Singh, A., Rakhra, M., Kler, R., Aggarwal, G., Jairath, K.: Ai-driven
threat intelligence for proactive cybersecurity in enterprise networks. In: 2025 International
Conference on Networks and Cryptology (NETCRYPT). pp. 1720–1725.
IEEE (2025)
4. Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient
over f1 score and accuracy in binary classification evaluation. BMC Genomics 21,
1–13 (2020)
5. Esteva, A., Robicquet, A., Ramsundar, B., et al.: A guide to deep learning in
healthcare. Nature Medicine 27(1), 25–36 (2021)
6. Ghassemi, M., Oakden-Rayner, L., Beam, A.L.: The false hope of current approaches
to explainable artificial intelligence in health care. The Lancet Digital
Health 3(11), e745–e750 (2021)
7. Gupta, S., Hamid, A.B.A., Nyamasvisva, T.E., Tyagi, N., Jain, V., Mun, N.K.,
Ather, D.: Enhanced agricultural decision-making: Machine learning approaches
for crop prediction and analysis in india. Jurnal Online Informatika 10(2), 407–
417 (2025)
8. Jain, V., Ather, D., Hamid, A.B.A., Kaur, B., Ugli, I.S.A., Manteghi, G.: Arduinobased
lifi-iot prototype for smart environment monitoring. In: 2025 Optical Communication,
Photonics, Telecommunications, and Intelligent Machine Applications
(OPTIMA). pp. 365–371. IEEE (2025)
9. Jain, V., Ather, D., Hamid, A.B.A., Sharma, R.R., Talipova, G., Manteghi, G.:
Secure arduino-based lifi communication for iot sensor networks. In: 2025 Optical
Communication, Photonics, Telecommunications, and Intelligent Machine Applications
(OPTIMA). pp. 189–194. IEEE (2025)
10. Johnson, K.W., Torres Soto, J., Glicksberg, B.S., et al.: Artificial intelligence in cardiology.
Journal of the American College of Cardiology 71(23), 2668–2679 (2020)
11. Khera, R., Haimovich, J., Hurley, N.C., et al.: Use of machine learning models to
predict death after acute myocardial infarction. JAMA 325(12), 1170–1180 (2021)
12. Khurramov, A., Irgashev, N., Ather, D., Kumar, R.: Hybrid lifi-wifi architecture for
high-speed vehicular communication in intelligent transportation systems. In: 2025
Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 324–331. IEEE (2025)
13. Knuuti, J., Wijns, W., Saraste, A., et al.: Esc guidelines for the diagnosis and
management of chronic coronary syndromes. European Heart Journal 41(3), 407–
477 (2020)
14. Lundberg, S.M., Erion, G., Chen, H., et al.: From local explanations to global
understanding with explainable ai for trees. Nature Machine Intelligence 2(1), 56–
67 (2020)
15. Nishanova, G., Ibragimova, O., Ather, D., Badhan, A.K.: Adaptive switching between
lifi and wifi for seamless connectivity in high-speed transport systems. In:
2025 Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 89–95. IEEE (2025)
16. Patel, M.R., Calhoon, J.H., Dehmer, G.J., et al.: Acc/aha/scai guideline for coronary
artery revascularization. Circulation 144(22), e18–e114 (2021)
17. Polat, K., Yosunkaya, S., Gunes, S.: A hybrid ensemble learning framework for
cardiovascular disease diagnosis. Biomedical Signal Processing and Control 79,
104070 (2023)
18. Raj, D., Sagar, A.K., Ather, D.: Optimizing vertical handover using multi-criteria
decision making in heterogeneous networks. Journal of Information Technology
Management 17(Special Issue on SI: Intelligent Security and Management), 63–86
(2025)
19. Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. New England
Journal of Medicine 380(14), 1347–1358 (2022)
20. Rudin, C.: Interpretable machine learning for high-stakes decisions. Nature Machine
Intelligence 4(1), 4–6 (2022)
21. Saito, T., Rehmsmeier, M.: The precision-recall plot is more informative than the
roc plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE
15(3), e0230114 (2020)
22. Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (xai): Toward
medical xai. IEEE Transactions on Artificial Intelligence 4(1), 113–127 (2023)
23. Topol, E.: Deep Medicine: How Artificial Intelligence Can Make Healthcare Human
Again. Basic Books (2021)
24. Virani, S.S., Alonso, A., Benjamin, E.J., et al.: Heart disease and stroke statistics—
2023 update. Circulation 147(8), e93–e621 (2023)
25. World Health Organization: Ethics and governance of artificial intelligence for
health (2023)
26. Yusupovich, S.Y., Sulliev, A.X., Ather, D., Singh, M., Kaur, J., Kaur, G.: Energyefficient
lifi transceivers for 6g-ready intelligent transportation systems. In: 2025
Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 497–503. IEEE (2025)
27. Zakirov, V., Kosimova, K., Ather, D., Singh, M., Kaur, J., Kaur, G.: Optical wireless
communication for high-density traffic: A lifi–vanet integrated approach. In:
2025 Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 162–167. IEEE (2025)
28. Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. CRC Press (2021)
detection using machine learning techniques: A review. Computers in Biology and
Medicine 137, 104654 (2022)
2. Arnett, D.K., Blumenthal, R.S., Albert, M.A., et al.: 2019 acc/aha guideline on the
primary prevention of cardiovascular disease. Circulation 143(8), e78–e91 (2021)
3. Ather, D., Singh, A., Rakhra, M., Kler, R., Aggarwal, G., Jairath, K.: Ai-driven
threat intelligence for proactive cybersecurity in enterprise networks. In: 2025 International
Conference on Networks and Cryptology (NETCRYPT). pp. 1720–1725.
IEEE (2025)
4. Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient
over f1 score and accuracy in binary classification evaluation. BMC Genomics 21,
1–13 (2020)
5. Esteva, A., Robicquet, A., Ramsundar, B., et al.: A guide to deep learning in
healthcare. Nature Medicine 27(1), 25–36 (2021)
6. Ghassemi, M., Oakden-Rayner, L., Beam, A.L.: The false hope of current approaches
to explainable artificial intelligence in health care. The Lancet Digital
Health 3(11), e745–e750 (2021)
7. Gupta, S., Hamid, A.B.A., Nyamasvisva, T.E., Tyagi, N., Jain, V., Mun, N.K.,
Ather, D.: Enhanced agricultural decision-making: Machine learning approaches
for crop prediction and analysis in india. Jurnal Online Informatika 10(2), 407–
417 (2025)
8. Jain, V., Ather, D., Hamid, A.B.A., Kaur, B., Ugli, I.S.A., Manteghi, G.: Arduinobased
lifi-iot prototype for smart environment monitoring. In: 2025 Optical Communication,
Photonics, Telecommunications, and Intelligent Machine Applications
(OPTIMA). pp. 365–371. IEEE (2025)
9. Jain, V., Ather, D., Hamid, A.B.A., Sharma, R.R., Talipova, G., Manteghi, G.:
Secure arduino-based lifi communication for iot sensor networks. In: 2025 Optical
Communication, Photonics, Telecommunications, and Intelligent Machine Applications
(OPTIMA). pp. 189–194. IEEE (2025)
10. Johnson, K.W., Torres Soto, J., Glicksberg, B.S., et al.: Artificial intelligence in cardiology.
Journal of the American College of Cardiology 71(23), 2668–2679 (2020)
11. Khera, R., Haimovich, J., Hurley, N.C., et al.: Use of machine learning models to
predict death after acute myocardial infarction. JAMA 325(12), 1170–1180 (2021)
12. Khurramov, A., Irgashev, N., Ather, D., Kumar, R.: Hybrid lifi-wifi architecture for
high-speed vehicular communication in intelligent transportation systems. In: 2025
Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 324–331. IEEE (2025)
13. Knuuti, J., Wijns, W., Saraste, A., et al.: Esc guidelines for the diagnosis and
management of chronic coronary syndromes. European Heart Journal 41(3), 407–
477 (2020)
14. Lundberg, S.M., Erion, G., Chen, H., et al.: From local explanations to global
understanding with explainable ai for trees. Nature Machine Intelligence 2(1), 56–
67 (2020)
15. Nishanova, G., Ibragimova, O., Ather, D., Badhan, A.K.: Adaptive switching between
lifi and wifi for seamless connectivity in high-speed transport systems. In:
2025 Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 89–95. IEEE (2025)
16. Patel, M.R., Calhoon, J.H., Dehmer, G.J., et al.: Acc/aha/scai guideline for coronary
artery revascularization. Circulation 144(22), e18–e114 (2021)
17. Polat, K., Yosunkaya, S., Gunes, S.: A hybrid ensemble learning framework for
cardiovascular disease diagnosis. Biomedical Signal Processing and Control 79,
104070 (2023)
18. Raj, D., Sagar, A.K., Ather, D.: Optimizing vertical handover using multi-criteria
decision making in heterogeneous networks. Journal of Information Technology
Management 17(Special Issue on SI: Intelligent Security and Management), 63–86
(2025)
19. Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. New England
Journal of Medicine 380(14), 1347–1358 (2022)
20. Rudin, C.: Interpretable machine learning for high-stakes decisions. Nature Machine
Intelligence 4(1), 4–6 (2022)
21. Saito, T., Rehmsmeier, M.: The precision-recall plot is more informative than the
roc plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE
15(3), e0230114 (2020)
22. Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (xai): Toward
medical xai. IEEE Transactions on Artificial Intelligence 4(1), 113–127 (2023)
23. Topol, E.: Deep Medicine: How Artificial Intelligence Can Make Healthcare Human
Again. Basic Books (2021)
24. Virani, S.S., Alonso, A., Benjamin, E.J., et al.: Heart disease and stroke statistics—
2023 update. Circulation 147(8), e93–e621 (2023)
25. World Health Organization: Ethics and governance of artificial intelligence for
health (2023)
26. Yusupovich, S.Y., Sulliev, A.X., Ather, D., Singh, M., Kaur, J., Kaur, G.: Energyefficient
lifi transceivers for 6g-ready intelligent transportation systems. In: 2025
Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 497–503. IEEE (2025)
27. Zakirov, V., Kosimova, K., Ather, D., Singh, M., Kaur, J., Kaur, G.: Optical wireless
communication for high-density traffic: A lifi–vanet integrated approach. In:
2025 Optical Communication, Photonics, Telecommunications, and Intelligent Machine
Applications (OPTIMA). pp. 162–167. IEEE (2025)
28. Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. CRC Press (2021)
Published
2025-10-10
How to Cite
Sachin Upadhyay, Anil Kumar Sagar, & Nihar Ranjan Roy. (2025). An Explainable Hybrid Ensemble-Based Analytical Framework for Interpretable and Early Prediction of Coronary Artery Disease. MATRIX Academic International Online Journal Of Engineering And Technology, 8(2), 36-50. Retrieved from https://maiojet.com/index.php/matrix/article/view/101
Section
Articles
