Efficient Model for Early Prediction of Heart Disease Using Ensemble Technique

Authors

  • Ankush Hutke Department of Computer Engineering, MCT’s Rajiv Gandhi Institute of Technology, University of Mumbai, Maharashtra, India
  • Jyoti Deshmukh Department of Computer Engineering, MCT’s Rajiv Gandhi Institute of Technology, University of Mumbai, Maharashtra, India

DOI:

https://doi.org/10.46604/peti.2024.14787

Keywords:

heart disease prediction, ensemble learning, SMOTE, CatBoost classifier

Abstract

The growing global burden of cardiovascular diseases has created an urgent need for advanced early-detection devices that revolutionize preventive cardiology. This research presents a novel two-stage ensemble (TSE) learning framework that outperforms traditional machine learning methods by integrating multiple complex algorithms, including random forest, adaptive boosting, gradient boosting machine, light gradient boosting machine, and extra trees classifier, into a highly accurate predictive system in stage 1. The approach incorporates a sophisticated preprocessing pipeline with feature scaling and the synthetic minority oversampling technique SMOTE to address the class imbalance and ensure robust input data quality. The model optimizes a meta-learner for enhanced predictions by leveraging meta-features derived from various classifiers. The developed TSE model, utilizing the CatBoost classifier in stage 2, achieved average accuracies of 92.5% and 90.19% on the Cleveland and Statlog datasets, respectively. This comprehensive ensemble framework significantly advances clinical decision support for early detection and intervention in cardiovascular disease.

References

C. Zhou, et al., “A Comprehensive Review of Deep Learning-Based Models for Heart Disease Prediction,” Artificial Intelligence Review, vol. 57, article no. 263, 2024.

J. Shafi, et al., “Prediction of Heart Abnormalities Using Deep Learning Model and Wearable-devices in Smart Health Homes,” Multimedia Tools and Applications, vol. 81, no.1, pp. 543-557, 2022.

P. Bizimana, Z. Zhang, M. Asim, and A. El-Latif, “An Effective Machine Learning-Based Model for Early Heart Disease Prediction,” BioMed Research International, vol. 2023, no. 1, article no. 3531420, 2023.

K. M. Almustafa, “Prediction of Heart Disease and Classifiers’ Sensitivity Analysis,” BMC Bioinformatics, vol. 21, article no. 278, 2020.

S. P. Barfungpa, H. K. M. Sarma, L. Samantaray, “An Intelligent Heart Disease Prediction System Using Hybrid Deep Dense Aquila Network,” Biomedical Signal Processing and Control, vol. 84, article no. 104742, 2023.

A. Rehman, et. al., “HCDP-DELM: Heterogeneous Chronic Disease Prediction with Temporal Perspective Enabled Deep Extreme Learning Machine,” Knowledge-Based Systems, vol. 284, article no. 111316, 2024.

A. Hutke and J. Deshmukh, “A Systematic Review of Machine Learning Approaches and Missing Data Imputation

Techniques for Predicting Heart Disease,” Proceedings of 2023 International Conference on Advanced Computing Technologies and Applications (ICATA), pp. 1-5, 2023.

C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, “Effective Heart Disease Prediction Using Machine Learning Techniques,” Algorithms, vol. 16, no. 2, article no. 88, 2023.

E. A. Ogundepo and W. B. Yahya, “Performance Analysis of Supervised Classification Models on Heart Disease Prediction,” Innovations in Systems and Software Engineering, vol. 19, pp. 129–144, 2023.

M. Zeng, “The Prediction of Heart Failure Based on Four Machine Learning Algorithms,” Highlights in Science, Engineering and Technology, vol. 39, pp. 1377–1382, 2023.

A. Khan, M. Qureshi, M. Daniyal, and K. Tawiah, “A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction,” Health & Social Care in the Community, vol. 2023, no. 1, article no.1406060, 2023.

D. Shah, S. Patel, and S. K. Bharti, “Heart Disease Prediction Using Machine Learning Techniques,” SN Computer Science, vol. 1, article no. 345, 2020.

A. Garg, B. Sharma, and R. Khan, “Heart Disease Prediction Using Machine Learning Techniques,” IOP Conference Series: Materials Science and Engineering, vol. 1022, article no. 012046, 2021.

V. Shorewala, “Early Detection of Coronary Heart Disease Using Ensemble Techniques,” Informatics in Medicine Unlocked, vol. 26, article no. 100655, 2021.

G. Ahamad, et. al., “Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease,” Processes, vol. 11, no. 3, article no. 734, 2023.

P. Mahajan, S. Uddin, F. Hajati, M. A. Moni, “Ensemble Learning for Disease Prediction: A Review,” Healthcare, vol. 11, no. 12, article no. 1808, 2023.

P. Mahajan, S. Uddin, F. Hajati, M. A. Moni, “A Comparative Evaluation of Machine Learning Ensemble Approaches for Disease Prediction Using Multiple Datasets,” Health and Technology, vol. 14, pp. 597–613, 2024.

A. Hutke and J. Deshmukh, “A Novel Hybrid Approach for Feature Selection in Cardiovascular Risk Assessment”, Advances in Technology Innovation, vol. 9, no. 4, pp. 319-331, 2024.

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Published

2025-06-06

How to Cite

[1]
Ankush Hutke and Jyoti Deshmukh, “Efficient Model for Early Prediction of Heart Disease Using Ensemble Technique”, Proc. eng. technol. innov., Jun. 2025.

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Section

Articles