Open Access

Prediction of Heart Disease Using Ensemble Machine Learning Techniques

https://doi.org/10.55640/ijidml-v03i09-01

PDF

Mr. Madhav Sharma ¹ ,

⁴ Assistant Professor, Department of Computer Science and Applications, Mandsaur(M.P.) 458001, India

Abstract

In this current world, keep hearing about heart disease problems every day and about the deaths due to them, making heart disease a major contributor to the crucial mortality rate worldwide. According to the World Health Organization (WHO), an estimated 17.9 million individuals die from cardiovascular diseases (CVDs) each year. The identification of cardiovascular disease states, including cardiac arrhythmia and coronary heart disease, based on traditional clinical data analysis is still a big challenge. The early diagnosis of cardiac disease can enable timely medical treatment and save many lives. The use of machine learning (ML) algorithms enables intelligent decision-making and accurate disease prediction by identifying complex patterns in healthcare data. This study adopted the following preprocessing methods for the UCI Heart Disease dataset: missing-value treatment, duplicate removal, noise reduction, one-hot encoding, Z-score normalization, and SMOTE data balancing. The proposed XGBoost model was developed for heart disease risk assessment and evaluated using accuracy, precision, recall, and F1-score. The proposed model achieved 99.8% accuracy, 99.7% precision, 99.9% recall, and 99.6% F1-score, demonstrating its effectiveness and reliability for accurate heart disease prediction.

Keywords

Explainable Artificial Intelligence (XAI), Heart Disease, Ensemble Learning

References

S. Akinola, R. Leelakrishna, and V. Varadarajan, “Enhancing cardiovascular disease prediction: A hybrid machine learning approach integrating oversampling and adaptive boosting techniques,” AIMS Med. Sci., vol. 11, no. 2, pp. 58–71, 2024, doi: 10.3934/medsci.2024005.

S. C. Patra, B. U. Maheswari, and P. B. Pati, “Forecasting Coronary Heart Disease Risk With a 2-Step Hybrid Ensemble Learning Method and Forward Feature Selection Algorithm,” IEEE Access, vol. 11, pp. 136758–136769, 2023, doi: 10.1109/ACCESS.2023.3338369.

A. Jaiswal, M. Singh, and N. Sachdeva, “Empirical Analysis of Heart Disease Prediction Using Deep Learning,” in 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, May 2023, pp. 1–9. doi: 10.1109/ACCAI58221.2023.10201235.

P. Ghosh et al., “Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques,” IEEE Access, vol. 9, pp. 19304–19326, 2021, doi: 10.1109/ACCESS.2021.3053759.

M. Beri, K. S. Gill, and N. Sharma, “Advanced Machine Learning Techniques for Early Prediction of Heart Disease: A Comprehensive Analysis,” in 2024 4th International Conference on Sustainable Expert Systems (ICSES), IEEE, Oct. 2024, pp. 1219–1223. doi: 10.1109/ICSES63445.2024.10763007.

M. Pal, S. Parija, G. Panda, K. Dhama, and R. K. Mohapatra, “Risk prediction of cardiovascular disease using machine learning classifiers,” Open Med., vol. 17, no. 1, pp. 1100–1113, Jun. 2022, doi: 10.1515/med-2022-0508.

K. P. Joshi, M. L. Prasad, R. Natchadalingam, P. C. S. Reddy, S. Mukherjee, and G. C. Babu, “An Accurate Prediction of Coronary Heart Disease Using Ensemble Algorithms,” in IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023, 2023. doi: 10.1109/AIKIIE60097.2023.10390172.

H. El-Sofany, B. Bouallegue, and Y. M. A. El-Latif, “A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method,” Sci. Rep., vol. 14, no. 1, p. 23277, Oct. 2024, doi: 10.1038/s41598-024-74656-2.

M. Mahalakshmi and R. Rajakumari, “Delving into Heart Disease Prediction: A Comprehensive Review,” in 2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC), IEEE, Nov. 2023, pp. 01–07. doi: 10.1109/ICAIHC59020.2023.10430484.

K. Sravani et al., “Early Detection of Lung Cancer Using Convolutional Neural Networks and DNA Methylation Biomarker Analysis,” in Next-Generation Computing and Information Systems, London, India: CRC Press, 2026, pp. 250–258. doi: 10.1201/9781003648987-34.

H. J. Suleiman, I. R. A. Hamid, and O. R. Olaniran, “Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model,” Eng. Technol. Appl. Sci. Res., vol. 15, no. 3, pp. 22565–22572, 2025, doi: 10.48084/etasr.10048.

R. Lingam, “Agentic AI and Federated Learning for Privacy-Preserving Dental Imaging,” in 2026 Innovations in Machine, Engineering, and Digital Conference (IMED), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/IMED68921.2026.11484887.

K. K. Mohammed, “A Survey on Digital Health Care Data Analysis Techniques for Developing Machine Learning Models,” Int. J. Sci. Eng. Technol., vol. 13, no. 5, pp. 1–6, 2025.

D. P. Ramaraj and M. Mittal, “Deep Learning Models for Automated Medical Imaging: Accuracy, Ethics, And Deployment Challenges,” Int. J. Artif. Intell. Deep Learn., vol. 2, no. 1, pp. 11–31, June, 2025, doi: 10.34218/IJAIDL_02_01_002.

M. Swathy and K. Saruladha, “A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques,” ICT Express, vol. 8, no. 1, pp. 109–116, Mar. 2022, doi: 10.1016/j.icte.2021.08.021.

J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, pp. 107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.

S. N, P. K, C. A, and P. P, “Artificial Intelligence and Pharmaceutical Discovery: A Comprehensive Review of Methods, Applications, and Future Directions,” Int. J. Sci. Res., vol. 14, no. 8, pp. 679–681, Aug. 2025, doi: 10.21275/SR25808215949.

M. S. Amin, Y. K. Chiam, and K. D. Varathan, “Identification of significant features and data mining techniques in predicting heart disease,” Telemat. Informatics, vol. 36, pp. 82–93, Mar. 2019, doi: 10.1016/j.tele.2018.11.007.

E. Maini, B. Venkateswarlu, B. Maini, and D. Marwaha, “Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India,” Med. J. Armed Forces India, vol. 77, no. 3, pp. 302–311, 2021, doi: 10.1016/j.mjafi.2020.10.013.

A. Najafi, A. Nemati, M. Ashrafzadeh, and S. Hashemkhani Zolfani, “Multiple-criteria decision making, feature selection, and deep learning: A golden triangle for heart disease identification,” Eng. Appl. Artif. Intell., vol. 125, p. 106662, Oct. 2023, doi: 10.1016/j.engappai.2023.106662.

A. V. S. R. Dantuluri, “A Scalable Deep Learning Analytics Pipeline for Converting Longitudinal Real-World Data Into Predictive Disease Trajectories,” IEEE Access, vol. 14, pp. 24871–24878, 2026, doi: 10.1109/ACCESS.2026.3663571.

N. Shah and A. Poonia, “Heart Disease Prediction System using Ensemble Machine Learning Technique,” in 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), IEEE, Aug. 2025, pp. 945–949. doi: 10.1109/ICSCSA66339.2025.11171051.

S. Kumar, A. Saundik, S. Sindhu, A. Chattraj, R. Pathania, and A. P. Singh, “Predicting Heart Disease using Machine Learning Approach,” in 2025 8th International Conference on Circuit, Power & Computing Technologies (ICCPCT), IEEE, Aug. 2025, pp. 1119–1124. doi: 10.1109/ICCPCT65132.2025.11176573.

P. Banerjee, N. T M, M. G. Paul, N. G. S. Krishna, and S. Agarwal, “A Novel Approach for Heart Disease Prediction Using Ensemble Machine Learning Techniques,” in 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC), IEEE, Feb. 2025, pp. 520–526. doi: 10.1109/ISACC65211.2025.10969413.

D. Purwanto, S. C. Hidayati, D. I. Ricoida, and K. A. Putri, “Optimized Machine Learning Models for Heart Disease Prediction: A Performance Analysis,” in 2024 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, Sep. 2024, pp. 559–562. doi: 10.1109/iSemantic63362.2024.10762500.

Z. Rustamov, J. Rustamov, M. S. Sultana, J. Ywei, V. Balakrishnan, and N. Zaki, “Cardiovascular Disease Prediction using Ensemble Learning Techniques: A Stacking Approach,” in 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), IEEE, Mar. 2023, pp. 93–98. doi: 10.1109/CSPA57446.2023.10087730.

S. Cyriac, S. R, N. Raju, and Y. Woon Kim, “Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning,” in 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), IEEE, Oct. 2022, pp. 1326–1331. doi: 10.1109/ICTC55196.2022.9952655.

A. B. Ambrews, E. G. Moung, A. Farzamnia, F. Yahya, S. Omatu, and L. Angeline, “Ensemble Based Machine Learning Model for Heart Disease Prediction,” in 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES), IEEE, Nov. 2022, pp. 1–6. doi: 10.1109/CIEES55704.2022.9990665.

J. Deshmukh, “Efficient Model for Early Prediction of Heart Disease Using Ensemble Technique,” Proc. Eng. Technol. Innov., vol. 30, pp. 66–78, Jun. 2025, doi: 10.46604/peti.2024.14787.

J. S. Mishra, N. K. Gupta, and A. Sharma, “Enhanced Heart Disease Prediction Using Machine Learning Techniques,” J. Intell. Syst. Internet Things, vol. 12, no. 2, pp. 19–33, 2024, doi: 10.54216/JISIoT.120202.

C. A. ul Hassan et al., “Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers,” Sensors, vol. 22, no. 19, p. 7227, Sep. 2022, doi: 10.3390/s22197227.

D. Asif, M. Bibi, M. S. Arif, and A. Mukheimer, “Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization,” Algorithms, vol. 16, no. 6, p. 308, Jun. 2023, doi: 10.3390/a16060308.

International Journal of Intelligent Data and Machine Learning

Prediction of Heart Disease Using Ensemble Machine Learning Techniques

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles