Comparison of Ensemble learning algorithms in predicting heart disease

  • Bader N. Awedat computer science, faculty of Information Technology / Azzaytuna University, Libya
  • Ali M. Abumrfgh computer science, faculty of Information Technology / Azzaytuna University, Libya
DOI https://doi.org/10.58916/jhas.v8i3.188
Published: 2023-09-07
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Abstract

Abstract: The main objective of this research is enhancing accuracy of predictive analysis for cardiovascular diseases (CVDs) through the implementation of ensemble learning algorithms. Ensemble learning is a strong approach that combines predictions from multiple models to amelioration overall performance. In this research, we compare the effectiveness of three ensemble learning algorithms: Random Forest, AdaBoost, and Stacking. We evaluate their performance using five criteria: Recall, Precision, F-score, Roc Auc, and Accuracy. The obtained results indicate that the AdaBoost algorithm has achieved the highest performance in the field of diagnosis using the available data. This signifies the high effectiveness of this algorithm in disease prediction and diagnosis. It is also notable that the Stacking algorithm has demonstrated strong performance, particularly in comparison to the Random Forest algorithm. Other performance standards such as Accuracy, Recall, Cohen's kappa, F-measure, Precision, and Specificity also exhibit good performance for the different algorithms. The ROC Curve metric reveals that the AdaBoost algorithm has attained the highest value (97.64), indicating its capability to effectively discriminate between true and false instances.

Keywords: Bagging, Boosting, Ensemble learning, ROC curve, Stacking.

How to Cite

Bader N. Awedat, & Ali M. Abumrfgh. (2023). Comparison of Ensemble learning algorithms in predicting heart disease. Bani Waleed University Journal of Humanities and Applied Sciences, 8(3), 115-127. https://doi.org/10.58916/jhas.v8i3.188
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Issue

Vol. 8 No. 3 (2023): v8i32023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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