A  Stacking-Based Ensemble Model for Early Prediction of Cardiovascular Disease

Durga Thokala; Rajasekhar Reddy  M

Authors

Durga Thokala Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Andhra Pradesh, 522503, India.
Rajasekhar Reddy M Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Andhra Pradesh, 522503, India.

Keywords:

Cardiovascular disease, Machine learning, Ensemble learning

Abstract

Cardiovascular disease is now a leading cause of death in the world, requiring the development of advanced models for prediction to allow early detection and risk reduction. This study investigates examines machinery learning technique for Cardiovascular conditions using patient clinical and ECG data (age, cholesterol level, arterial pressure measures and ECG signals). The dataset that is recruited for experiment contains 303 instances. Various classification algorithms such as Logistic Regression, Decision Tree, Random Forest, SVM, Gradient Boosting, XGBoost, Extra Trees, AdaBoost, and various ensemble-based learning methods are implemented and evaluated. For the purpose of comparing performance. Feature normalization, class balancing by resampling is also included in data pre-processing, and cross validation. The ensemble models have superior performing evaluated in terms of accuracy, precision and recall for a single classifier, in particular Stacking Model and Gradient Boosting. The Stacking Model highest accuracy was (97.33%) with AUC-ROC 99.00% then Gradient Boosting with accuracy 97.00% and AUC-ROC 99.16%. These findings highlight the potential of ensemble-based ML techniques in predicting CVD, ultimately leading to a strong instrument for improving patient care and enabling early diagnosis through AI-analytics.

References

M. Lei, Y. Li, L. Cheng, N. Tang, J. Song, M. Song, Q. Su, M. Liu, S. Fu, F. Lin, Y. Gao, Stress hyperglycemia ratio as a biomarker for early mortality risk stratification in cardiovascular disease: a propensity-matched analysis, Cardiovasc. Diabetol. 24(1) (2025) 286.

A. Sharma, S. Dhanka, A. Kumar, M. Nain, B. Dhanka, V.K. Bhardwaj, S. Maini, A.S. Arora, A systematic review on machine learning intelligent systems for heart disease diagnosis, Arch. Comput. Methods Eng. 32(7) (2025) 4303 – 4329.

A. Tiwari, A. Chugh, A. Sharma, Ensemble framework for cardiovascular disease prediction, Comput. Biol. Med. 146 (2022) 105624.

H. Mohammedqasim, R.A. Mohammedqasem, O. Ata, E.I. Alyasin, Diagnosing coronary artery disease on the basis of hard ensemble voting optimization, Medicina. 58(12) (2022) 1745.

A. Pandey, K.V. Patel, M.W. Segar, M.D. Shapiro, C.M. Ballantyne, S.S. Virani, V. Nambi, E.D. Michos, M.J. Blaha, K. Nasir, M. Cainzos-Achirica, C. Ayers, B.D. Westenbrink, J.L. Flores-Guerrero, S.J.L. Bakker, M.A. Connelly, R.P.F. Dullaart, A. Rohatgi, Association of high-density lipoprotein parameters and risk of heart failure: a multicohort analysis, JACC Heart Fail. 12(7) (2024) 1242 – 1253.

R. Valarmathi, T. Sheela, Heart disease prediction using hyper parameter optimization (HPO) tuning, Biomed. Signal Process. Control. 70 (2021) 103033.

G. Gul, I.A. Korejo, D.N. Hakro, H. Alqahtani, A. Abbasi, M. Babar, O. Al Rahbi, N.I. Ali, Machine learning and ensemble methods for cardiovascular disease prediction: a systematic review of approaches, performance trends, and research challenges, Computers. 15(1) (2026) 25.

S. Mondal, R. Maity, Y. Omo, S. Ghosh, A. Nag, An efficient computational risk prediction model of heart diseases based on dual-stage stacked machine learning approaches, IEEE Access. 12 (2024) 7255 – 7270.

V. Shorewala, Early detection of coronary heart disease using ensemble techniques, Inform. Med. Unlocked. 26 (2021) 100655.

M. Dubey, J. Tembhurne, R. Makhijani, Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data, Multimed. Tools Appl. 83(37) (2024) 85139 – 85168.

S.T. Aarthy, J. Mazher Iqbal, A novel deep learning approach for early detection of cardiovascular diseases from ECG signals, Med. Eng. Phys. 125 (2024) 104111.

M. Swathy, K. Saruladha, A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques, ICT Express. 8(1) (2022) 109 – 116.

P. Guleria, P. Naga Srinivasu, S. Ahmed, N. Almusallam, F.K. Alarfaj, XAI framework for cardiovascular disease prediction using classification techniques, Electronics. 11(24) (2022) 4086.

G. Niveditha, B.U. Maheswari, R.P. de Prado, Elevating maternal healthcare: synergy of cardiotocography, machine learning models and interpretive analysis, Procedia Comput. Sci. 258 (2025) 2787 – 2797.

B.K. Raghupathy, M.R. Reddy, P. Theeda, E. Balasubramanian, R.K. Namachivayam, M. Ganesan, Harnessing explainable artificial intelligence (XAI) based SHAPLEY values and ensemble techniques for accurate Alzheimer's disease diagnosis, Eng. Technol. Appl. Sci. Res. 15(2) (2025) 20743 – 20747.

A. Alqahtani, S. Alsubai, M. Sha, L. Vilcekova, T. Javed, Cardiovascular disease detection using ensemble learning, Comput. Intell. Neurosci. 2022(1) (2022) 5267498.

S. Tuly, S. Majumder, A. Chowdhury, A. Habib, S.A. Suha, Predicting heart disease risk with lifestyle factors: uncovering vital predictors via feature selection and machine learning techniques, Proceedings of the 3rd International Conference on Computing Advancements. (2024) 466 – 473.

I.D. Mienye, Y. Sun, Z. Wang, An improved ensemble learning approach for the prediction of heart disease risk, Inform. Med. Unlocked. 20 (2020) 100402.

S. Mohapatra, S. Maneesha, P.K. Patra, S. Mohanty, Heart diseases prediction based on stacking classifiers model, Procedia Comput. Sci. 218 (2023) 1621 – 1630.

A. Arun, R.P. Singh, S. Kanchan, Predicting the coronary heart disease risk over the next decade through ensemble methodologies, 2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT). (2024) 1 – 6.