Ensemble Machine Learning with SHAP Interpretability for Predicting Unmet Contraceptive Needs in Ethiopia

Unmet contraceptive needs remain a critical challenge in global reproductive health, especially in developing countries like Ethiopia, where access to family planning is limited. Women who wish to delay or avoid pregnancy but do not use contraception face increased risks of unintended pregnancies, unsafe abortions, and adverse maternal outcomes. This study aims to develop and evaluate an ensemble machine learning model, enhanced with Explainable AI techniques, to accurately identify women at risk of unmet contraceptive needs, thereby supporting informed and transparent decision-making.Data from the 2011 and 2016 Ethiopian Demographic and Health Surveys were used. Ensemble models, including Random Forest, Categorical Boosting, Extreme Gradient Boosting, and Light Gradient Boosting Machine, were trained on 21 key features selected through Recursive Feature Elimination. A hybrid SMOTE-Tomek sampling technique addressed class imbalance. Stratified train-validation-test splits ensured robust performance evaluation.Extreme Gradient Boosting emerged as the best-performing model, achieving 96.56% accuracy, 97.59% precision, 95.99% recall, and a 96.53% F1-score in cross-validation. On the test set, it maintained strong results with 95.55% accuracy and a 90.90% F1-score, outperforming Logistic Regression and Support Vector Machine. Receiver Operating Characteristic curve analysis confirmed its excellent classification (AUC = 0.99). SHapley Additive exPlanations analysis highlighted key predictors driving Extreme Gradient Boosting’s predictions, including contraceptive information exposure, prior family planning use, pregnancy intention, decision-making autonomy, and fertility preferences.The findings provide interpretable, data-driven insights for targeted reproductive health interventions. Integrating such predictive models into real-time health systems may enhance family planning strategies and help achieve Ethiopia’s 2030 health goals.