Predicting health facility deliveries using explainable machine learning in Sidama Region, Ethiopia: A prospective...

Health facility delivery (HFD) is a key intervention for reducing maternal and neonatal morbidity and mortality. However, a substantial proportion of women in Ethiopia continue to give birth at home. Early identification of women at risk of home delivery is essential to support targeted maternal health interventions.

This study aimed to predict HFD utilization using machine learning (ML) models and to identify key determinants of delivery service uptake in the Sidama Region of Ethiopia.

A prospective cohort study was conducted among 3855 pregnant women who initiated antenatal care (ANC) in public health facilities across 4 districts of the Sidama Region between January 2021 and January 2025.

Data were analyzed using the R software version 4.3.1 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria). Predictive models - including logistic regression, random forest, gradient boosting, and extreme gradient boosting (XGBoost) were developed to predict HFD utilization. Model performance was assessed using accuracy, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) were used to identify the most influential predictors.

Overall, 59.2% of women delivered in health facilities, while 40.8% delivered at home. Among the evaluated models, XGBoost demonstrated the highest predictive performance, achieving an accuracy of 86.9% (95% confidence interval (CI): 85.6-88.1) and an AUC-ROC of 0.91 (95% CI: 0.90-0.93). SHAP analysis identified place of residence, maternal education, timing of ANC initiation, parity, and distance to the nearest health facility as the most influential predictors. Rural residence, lower educational attainment, late ANC initiation, higher parity, and greater distance to health facilities were associated with a lower likelihood of HFD utilization.

Despite improvements in HFD utilization, a substantial proportion of women in the Sidama Region continue to deliver at home. ML models offer a robust approach for identifying women at high risk of home delivery and supporting targeted, data-driven interventions. Strategies that promote early ANC engagement, maternal education, improved geographic access to health facilities, and integration of predictive analytics into health systems may enhance HFD utilization.