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A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets

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posted on 2024-08-13, 03:23 authored by P Mahajan, S Uddin, F Hajati, MA Moni, Ergun GideErgun Gide
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.

History

Volume

14

Issue

3

Start Page

597

End Page

613

Number of Pages

17

eISSN

2190-7196

ISSN

2190-7188

Publisher

Springer Science and Business Media LLC

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2024-02-26

Era Eligible

  • Yes

Journal

Health and Technology

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