Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.

Purpose: Machine learning models are used to develop and improve various disease prediction systems. Ensemble learning is a machine learning technique that combines many classifiers to increase performance by making more accurate predictions than a single classifier. Although several researchers have employed ensemble techniques for disease prediction, a comprehensive comparative study of these techniques still needs to be provided. Methods: Using 16 disease datasets from Kaggle and the UCI Machine Learning Repository, this study compares the performance of 15 variants of ensemble techniques for disease prediction. The comparison was performed using six performance measures: accuracy, precision, recall, F1 score, AUC (Area Under the receiver operating characteristics Curve) and AUPRC (Area Under the Precision-Recall Curve). Results: Stacking variant of Multi-level stacking showed superior disease prediction performance compared with other bagging and boosting variants, followed by another stacking variant (Classical stacking). Overall, stacking outperformed bagging and boosting for disease prediction. Logit Boost showed the worst performance. Conclusion: The findings of this study can help researchers select an appropriate ensemble approach for future studies focusing on accurate disease prediction.