Supplementary file 1_Machine learning–based ecological prediction of maternal mortality ratio in malaria-endemic Africa.docx

Inverse correlations were observed with HDI (r = −0.75; 95% CI, −0.83 to −0.63), female primary completion (r = −0.67; 95% CI, −0.79 to −0.52), and female secondary enrolment (r = −0.68; 95% CI, −0.77 to −0.57). Temporal-holdout permutation importance identified HDI and stillbirth rate as the leading contributors to predictive performance, whereas female malaria prevalence and malaria incidence had smaller positive importances.

Country-specific R² values ranged from −0.202 to 0.891, indicating substantial between-country variation in model performance. The SVR model achieved R² = 0.978, MAE = 27.4, and RMSE = 44.1 under the random country-year holdout, and R² = 0.940, MAE = 44.6, and RMSE = 64.7 under temporal validation. When entire countries were held out using 5-fold grouped cross-validation, pooled out-of-fold performance decreased to R² = 0.524, MAE = 158.0, and RMSE = 215.7.</p>Conclusion and recommendations<p>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.</p>