Machine Learning Approaches for Early Detection of Malaria in Sub-Saharan Africa

Emeka Okafor

Amina Yusuf

Chukwuemeka Nwachukwu

Abstract

This study investigates the application of supervised machine learning algorithms — including Random Forest, Support Vector Machines, and Gradient Boosting — to the early diagnosis of malaria using clinical and haematological data collected from tertiary hospitals across Sub-Saharan Africa. A dataset of 14,200 patient records was used for training and evaluation. The proposed ensemble model achieved a sensitivity of 94.3% and specificity of 91.7%, outperforming conventional microscopy-based screening in resource-constrained settings. The findings suggest that low-cost, deployable ML pipelines can significantly reduce diagnostic delays and improve patient outcomes in endemic regions.

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References

1. Olayemi, A. et al. (2023). ML in tropical disease diagnosis. *J. Biomed. Inform.*, 44(2), 112–120. 2. WHO (2024). World Malaria Report. Geneva: World Health Organization.

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(Okafor et al., 2025)

(Okafor et al. 45-67)

(Okafor et al. 2025)

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Okafor, E., Yusuf, A., & Nwachukwu, C. (2025). Machine Learning Approaches for Early Detection of Malaria in Sub-Saharan Africa. African Journal of Health Informatics, 12(3), 45-67.

Okafor, Emeka, et al. "Machine Learning Approaches for Early Detection of Malaria in Sub-Saharan Africa." *African Journal of Health Informatics*, vol. 12, no. 3, 2025, pp. 45-67.

Okafor, Emeka, Amina Yusuf, and Chukwuemeka Nwachukwu. 2025. "Machine Learning Approaches for Early Detection of Malaria in Sub-Saharan Africa." *African Journal of Health Informatics* 12 (3): 45-67.

@article{okafor2025machine,
  author = {Okafor, Emeka and Yusuf, Amina and Nwachukwu, Chukwuemeka},
  title = {Machine Learning Approaches for Early Detection of Malaria in Sub-Saharan Africa},
  year = {2025},
  journal = {African Journal of Health Informatics},
  volume = {12},
  number = {3},
  pages = {45-67},
  issn = {2382-5014},
}

Publication Details

Published	23 Mar 2026
Year	2025
Journal	African Journal of Health Informatics
Volume	12
Issue	3
Pages	45-67
Language	EN
Views	0

Keywords

machine learning malaria sub-saharan africa disease detection random forest

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