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A Machine Learning Approach to the Prediction of Malaria in Under-five Children: Analysis of the 2021 Nigerian Malaria Indicator Survey

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Machine learning models predict malaria status in children, indicating Random Forest and SVM outperform others.

Random Forest achieves 79% accuracy, making it the top-performing machine learning model for malaria prediction.
Support Vector Machines demonstrate strong accuracy and AUC, offering alternative predictive power for malaria in children.
The analysis utilized the 2021 Nigeria Malaria Indicator Survey, involving over 10,000 children tested with RDT.
These findings underscore the potential for improved malaria interventions through targeted predictive analytics.

Mthethwa et al. (2025) studied this question.

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