Machine learning-based ecological prediction of maternal mortality ratio in malaria-endemic Africa.

Background: Malaria in pregnancy remains a significant public health concern in sub-Saharan Africa. It may contribute to population-level maternal mortality through adverse maternal outcomes. This study aims to develop a machine learning framework to predict maternal mortality ratio (MMR) across African countries. Methods: A supervised machine learning framework was applied to identified open source, country-level data across 52 African countries through (2000-2020). MMR was estimated using Support Vector Regression (SVR) model. Model performance was evaluated using a random 70/30 split of country-year observations, temporal block sensitivity analysis applied using 2000-2016 for training and 2017-2020 for testing and 5-fold grouped cross-validation in which entire countries were held out from model training. Results: MMR was positively correlated with stillbirth rate ( Conclusion and recommendations: The findings of this study indicate that MMR in African countries is influenced by malaria burden and strongly shaped by reproductive, and socioeconomic factors. Reducing maternal mortality in Africa requires the implementation of comprehensive and effective maternal health policy approach that integrates malaria control strategies, malaria-climate surveillance and maternal health literacy programs, alongside strengthening governance, improving institutional capacity, and enhancing health-system performance.