Indian Journal of Science and Technology
Year: 2023, Volume: 16, Issue: 20, Pages: 1469-1476
P Muthulakshmi1*, M Parveen2, P Rajeswari3
1Assistant Professor, Cauvery College for Women (Autonomous), [Affiliated to Bharathidasan University], Trichy, India
2Professor, Cauvery College for Women (Autonomous), [Affiliated to Bharathidasan University], Trichy, India
3Associate Professor, Cauvery College for Women (Autonomous), [Affiliated to Bharathidasan University], Trichy, India
Email: [email protected]
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
© 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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