Predicting utilization of emergency contraceptives in ethiopia and identifying its predictors using machine learning

Predicting utilization of emergency contraceptives in ethiopia and identifying its predictors using machine learning. Download PDF Abstract Despite policy support, inappropriate use of emergency contraception in Ethiopia contributes to high rates of unintended pregnancy and maternal mortality. Traditional statistical analyses have struggled to identify complex predictors.

This study used machine learning and Explainable AI to improve the prediction and interpretability of emergency contraception use. We analyzed data from 2,334 women in the PMA Ethiopia 2023 survey. Eight ML algorithms were tested to predict past-year emergency contraception use (4.4% prevalence), and the SMOTE was used to address class imbalance and SHAP values for interpretation.

Logistic Regression on SMOTE data achieved the best performance (AUC-ROC: 0.85; Recall: 0.85; precision:0.72). The most important predictor was emergency contraception 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.

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