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<p>The super-learning ensemble algorithm has been demonstrated in the literature to improve classification tasks. However, limited works have been presented in the literature on investigating the performance of the super-learning ensemble algorithm on various benchmark datasets of diseases. This study, therefore, seeks to investigate and establish the effectiveness of the super learning algorithm in the binary classification of diseases. Five binary-class benchmark datasets of diseases from Kaggle and UCI repositories were considered in this study. Results show that the super-learning algorithm produces a consistently improved classification model over the base learning ensemble algorithms or as good as the base learning ensemble algorithms. On the breast cancer dataset, the super-learning algorithm achieved 97.7% accuracy and sensitivity of 0.968, which is the same performance as the best ensemble base-learner (AdaBoost). Also, on the diabetes dataset, the super-learning algorithm achieved 80.7% accuracy and a sensitivity of 0.834, while the best base ensemble learner (Bagging) has an accuracy of 80.3% and a sensitivity of 0.781. For heart failure data, the super-learner model algorithm achieved 87.7% accuracy and a sensitivity of 0.859 while the best base ensemble learner (AdaBoost) has 86.6% accuracy and 0.841 sensitivity. In the prostate cancer dataset, the super-learner algorithm achieved 54.7% accuracy and a sensitivity of 0.519 while the best base ensemble learner (Random Forest) achieved 53.0% accuracy and 0.410 sensitivity. Lastly, on the stroke dataset, the super-learning algorithm, however, achieved 96.7% accuracy and a sensitivity of 0.950 while the best base ensemble learner (Random Forest) has an accuracy of 96.8% and a sensitivity of 0.944. The performance of the work presented confirms that the super-learner algorithm has the ability to better the classification performance of the base learning ensemble algorithms or perform at the same level of accuracy with them for binary classification of diseases tasks.</p>

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Investigating the Performance of the Super-Learning Ensemble Algorithm in the Binary Classification of Diseases

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Toluwase Ayobami Olowookere,
  • Aanuoluwa Oyebola Adio,
  • Godbless Uwen Peter,
  • Olufunmilayo I. Olopade,
  • Benjamin S. Aribisala

摘要

The super-learning ensemble algorithm has been demonstrated in the literature to improve classification tasks. However, limited works have been presented in the literature on investigating the performance of the super-learning ensemble algorithm on various benchmark datasets of diseases. This study, therefore, seeks to investigate and establish the effectiveness of the super learning algorithm in the binary classification of diseases. Five binary-class benchmark datasets of diseases from Kaggle and UCI repositories were considered in this study. Results show that the super-learning algorithm produces a consistently improved classification model over the base learning ensemble algorithms or as good as the base learning ensemble algorithms. On the breast cancer dataset, the super-learning algorithm achieved 97.7% accuracy and sensitivity of 0.968, which is the same performance as the best ensemble base-learner (AdaBoost). Also, on the diabetes dataset, the super-learning algorithm achieved 80.7% accuracy and a sensitivity of 0.834, while the best base ensemble learner (Bagging) has an accuracy of 80.3% and a sensitivity of 0.781. For heart failure data, the super-learner model algorithm achieved 87.7% accuracy and a sensitivity of 0.859 while the best base ensemble learner (AdaBoost) has 86.6% accuracy and 0.841 sensitivity. In the prostate cancer dataset, the super-learner algorithm achieved 54.7% accuracy and a sensitivity of 0.519 while the best base ensemble learner (Random Forest) achieved 53.0% accuracy and 0.410 sensitivity. Lastly, on the stroke dataset, the super-learning algorithm, however, achieved 96.7% accuracy and a sensitivity of 0.950 while the best base ensemble learner (Random Forest) has an accuracy of 96.8% and a sensitivity of 0.944. The performance of the work presented confirms that the super-learner algorithm has the ability to better the classification performance of the base learning ensemble algorithms or perform at the same level of accuracy with them for binary classification of diseases tasks.