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Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features

Jan 28, 2026 |

By Farai Mlambo, Panashe Nyengera, Hilary Takunda Takawira

| preprints.org

Submitted: 26 January 2026 You are already at the latest version Sub-Saharan Africa continues to shoulder the heaviest burden of malaria. The 2024 World Health Organisation (WHO) malaria report highlighted that Africa contributed an alarming 94% of the global cases and 95% of the deaths.

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Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features

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