Predicting Utilization of Emergency Contraceptive Usage in Ethiopia and Identifying Its Predictors Using Machine Learning

Abstract Despite policy support, inappropriate use of emergency contraception (EC) in Ethiopia contributes to high rates of unintended pregnancy and maternal mortality. Traditional analyses have struggled to identify complex predictors. This study used machine learning (ML) and Explainable AI (XAI) to improve the prediction and interpretability of EC use. We analyzed data from 2,334 women in the PMA Ethiopia 2023 survey. Eight ML algorithms were tested to predict past-year EC use (4.4% prevalence), with SMOTE used to address class imbalance and SHAP values for interpretation. Logistic Regression on SMOTE data achieved the best performance (AUC-ROC: 0.848; Recall: 0.85). The most important predictor was EC awareness (“heard_emergency”), followed by media exposure and family planning discussions at health facilities. Conversely, recent reproductive events such as unintended pregnancy were linked to non-use. Static demographic factors showed poor predictive value. Findings highlight that knowledge gaps, not poverty or access, are key barriers to EC use. Tailored media campaigns and routine health counseling could enhance EC uptake. ML and XAI offer powerful tools for guiding targeted reproductive health interventions.