• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 20, Pages: 1469-1476

Original Article

Prediction of Heart Disease using Ensemble Learning

Received Date:26 November 2022, Accepted Date:12 March 2023, Published Date:19 May 2023


Objectives: To propose a Bagging ensemble method to predict heart disease at early stages. The main focus of this research is to increase the prediction accuracy in a model. Methods: The proposed system is experimented with by using the Cleveland datasets collected from the UCI repository. The dataset consists of 14 attributes. In this dataset we applied different machine learning algorithms such as Decision tree, Naïve Bayes, Random Forest and SVM along with the proposed ensemble learning classifier. The entire dataset is trained upon the Pearson correlation coefficient selected features under the k-fold cross-validation setup. Final outcome is obtained by aggregating the prediction accuracy. Findings: The performance of the proposed method was validated using prediction accuracy and compared with the Machine learning algorithms and the ensemble models. The proposed method attains a higher classification accuracy of 95.33% than all other methods. Novelty: A novel ensemble method has been proposed with a better accuracy in early predicting of the heart disease.

Keywords: Ensemble Model; Machine Learning; Prediction; Accuracy; Kfold cross validation


  1. Pal M, Parija S, Panda G, Dhama K, Mohapatra RK. Risk prediction of cardiovascular disease using machine learning classifiers. Open Medicine. 2022;17(1):1100–1113. Available from: https://doi.org/10.1515/med-2022-0508
  2. Alqahtani A, Alsubai S, Sha M, Vilcekova L, Javed T. Cardiovascular Disease Detection using Ensemble Learning. Computational Intelligence and Neuroscience. 2022;2022:1–9. Available from: https://doi.org/10.1155/2022/5267498
  3. El-Hasnony IM, Elzeki OM, Alshehri A, Salem H. Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction. Sensors. 2022;22(3):1184. Available from: https://doi.org/10.3390/s22031184
  4. Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MRI, et al. The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare. 2022;10(6):1137. Available from: https://doi.org/10.3390/healthcare10061137
  5. Gupta P, D SD. Improving the Prediction of Heart Disease Using Ensemble Learning and Feature Selection. International Journal of Advances in Soft Computing and its Applications. 2022;14(2):37–40. Available from: http://www.i-csrs.org/Volumes/ijasca/2022.02.03.pdf
  6. Peng Y, Wu Z, Jiang J. A novel feature selection approach for biomedical data classification. Journal of Biomedical Informatics. 2010;43(1):15–23. Available from: https://doi.org/10.1016/j.jbi.2009.07.008
  7. Thangam M, Bhuvaneswari A. Exponential kernelized feature map Theil-Sen regression-based deep belief neural learning classifier for drift detection with data stream. International Journal of Advanced Technology and Engineering Exploration. 2022;9(90):663–675. Available from: https://doi.org/10.19101/IJATEE.2021.874851
  8. Kumar SRS, Fatima AS, Thomas. Heart Disease Prediction using Ensemble Learning Method. International Journal of Recent Technology and Engineering. 2020;(9) 2277–3878. Available from: https://www.ijrte.org/wp-content/uploads/papers/v9i1/A2997059120.pdf
  9. Mienye ID, Sun Y, Professor Z, Wang. An improved ensemble learning approach for the prediction of heart disease risk”. Informatics in Medicine Unlocked. 2020;20:100402. Available from: https://doi.org/10.1016/j.imu.2020.100402
  10. Alim MA, Habib S, Farooq Y, Rafay A. Robust Heart Disease Prediction: A Novel Approach based on Significant Feature and Ensemble learning Model. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020;p. 1–5. Available from: https://doi.org/10.1109/iCoMET48670.2020.9074135
  11. David HBF. Impact of Ensemble Learning Algorithms Towards Accurate Heart Disease Prediction. ICTACT Journal On Soft Computing. 2020;(10) 3. Available from: https://doi.org/10.21917/ijsc.2020.0296
  12. Gupta A, Singh A. An optimal multi-disease prediction framework using hybrid machine learning. Kuwait Journal of Science. 2022;2022:1–13. Available from: https://doi.org/10.48129/kjs.splml.19321
  13. Doppala BP, Bhattacharyya D, Janarthanan M, Baik N. A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques. Journal of Healthcare Engineering. 2022. Available from: https://doi.org/10.1155/2022/2585235
  14. Khanna D, Sahu R, Baths V, Deshpande B. Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease. International Journal of Machine Learning and Computing. 2015;5(5):414–419. Available from: http://www.ijmlc.org/vol5/544-C039.pdf
  15. Mythili T, Mukherji D, Padalia N, Naidu A. A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL) International Journal of Computer Applications. 2013;68(16). Available from: https://research.ijcaonline.org/volume68/number16/pxc3887250.pdf
  16. Haq AU, Li JP, Memon MH, Nazir S, Sun R. A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms. Mobile Information Systems. 2018;p. 1–21. Available from: https://doi.org/10.1155/2018/3860146
  17. Gupta A, Jain V, Singh A. Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications. New Generation Computing. 2022;40(4):987–1007. Available from: https://doi.org/10.1007/s00354-021-00144-0


© 2023 Muthulakshmi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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