Background Health facility delivery is a critical strategy for reducing maternal and
neonatal mortalities. In Somalia, maternal mortality remains alarmingly high due to
socioeconomic disparities, geographic barriers, and limited healthcare infrastructure.
Machine learning (ML) offers a novel approach for predicting health facility deliveries
and identifying key determinants, enabling targeted interventions to improve maternal
health outcomes.
Methods This study analyzed data from the 2020 Somalia Demographic and Health
Survey (SDHS) involving 8,951 women aged 15–49 years. Seven ML algorithms,
Random Forest, XGBoost, Gradient Boosting, Logistic Regression, Support Vector
Machine, Decision Tree, and K-Nearest Neighbors, were evaluated for their ability to
predict health facility deliveries. Model performance was assessed using the accuracy,
precision, recall, F1-score, and AUROC. SHapley Additive exPlanations (SHAP) analysis
was employed to interpret the relative importance of predictors, including wealth
quintile, antenatal care (ANC) attendance, and residence type.
Results The Random Forest model achieved the highest performance, with an
accuracy of 82%, a recall of 84%, and an AUROC of 0.89. XGBoost and Gradient Boosting
followed with accuracies of 80% and 77%, respectively, and AUROC values of 0.89
and 0.86. Logistic regression and support vector machines demonstrated moderate
performance (accuracy: 72%, AUROC: 0.80–0.81). SHAP analysis identified the wealth
quintile as the most influential predictor, with women in the highest quintile being six
times more likely to deliver in health facilities than those in the lowest quintile (AOR:
6.01; 95% CI: 4.04–8.94). ANC attendance and residence type were also significant
contributors, with women attending four or more ANC visits (AOR: 4.82; 95% CI:
3.75–6.20) and urban residents were more likely to deliver in health facilities.
Conclusion Machine learning techniques, particularly Random Forest and SHAP
analyses, offer robust tools for predicting health facility deliveries and identifying critical
determinants. These findings underscore the potential of ML in designing targeted
data-driven interventions to improve maternal health outcomes in Somalia.