Machine learning models to predict the risk of malaria outbreaks using balanced climatic and non-climatic data

Abstract

Objective: To predict malaria outbreaks early and accurately using machine learning models when data presents challenges such as imbalance.

Methods: To predict the risk of malaria outbreaks, we applied six machine learning models—including naive Bayes, logistic regression, random forest, K-nearest neighbors, decision tree, and extreme gradient boosting—to a dataset of 8000 samples with 17 climatic and non-climatic features. To address class imbalance, we used the Synthetic Minority Over-sampling Technique algorithm. The models were evaluated using 10-fold cross-validation repeated 10 times to ensure robust validation.

Results: Extreme gradient boosting achieved the highest F1-score, improving from 0.926 on imbalanced data to 0.991 after balancing. While random forest, naive Bayes, and decision tree also improved their performance when balancing data with Synthetic Minority Over-sampling Technique (increasing F1-score from 0.893 to 0.931, from 0.765 to 0.962, and from 0.785 to 0.812, respectively), logistic regression and K-nearest neighbors did not show any improvement.

Conclusions: These findings demonstrate that integrating balanced climatic and non-climatic data with advanced machine learning can improve malaria outbreak risk prediction and support public health strategies.

Keywords

Malaria outbreak prediction / Machine learning / Data imbalance / Public health / Synthetic Minority Over-sampling Technique

Conflict of interest statement

The authors declare no conflicts of interest related to this study. No financial or personal relationships could influence the work presented in this manuscript.

Funding

This study was partially funded by the Conselleria d’Innovació, Universitats, Ciència i Societat Digital-Generalitat Valenciana (grant number CAICO/2023/032) and Ministerio de Ciencia, Innovación y Universidades (grant number AIA2025-163919-C54).

Authors' contributions

N.S.E. developed conceptualization, software, and visualization. P.L.C. developed study design, methodology, and the original draft. J.S.S. developed formal analysis, supervised the study, reviewed the writing, and acquired funding. All authors read and approved the final version of the manuscript.

Publisher’s note

The Publisher of the Journal remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by Lei Y, Zhang Q, Pan Y

Acknowledgments

We express our sincere gratitude to the Reviewers and the Editor for their valuable comments and suggestions.

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