Comparison of Ensemble learning algorithms in predicting heart disease

Bader N. Awedat; Ali M. Abumrfgh

doi:10.58916/jhas.v8i3.188

المؤلفون

Bader N. Awedat computer science, faculty of Information Technology / Azzaytuna University, Libya مؤلف
Ali M. Abumrfgh computer science, faculty of Information Technology / Azzaytuna University, Libya مؤلف

DOI:

https://doi.org/10.58916/jhas.v8i3.188

الكلمات المفتاحية:

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

الملخص

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.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

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