Exploring Machine Learning Insights into Long-Acting Reversible Family Planning Usage in Ethiopia: Analysis of the PMA (2021-2023) Dataset

Abstract Introduction : Ethiopia faces challenges in Long-Acting Reversible Family Planning (LARFP) adoption despite its efficacy. Traditional statistical methods have a limited capacity to capture nonlinear determinants. This study leverages machine learning (ML) to identify predictors of LARFP use using the 2021-2023 PMA Ethiopia dataset. Methods : A nationally representative sample of 9,763 women aged 15–49 was analyzed. Twenty-four variables across geographic, socioeconomic, healthcare access, and behavioral domains were preprocessed (handling missing values, encoding, and normalization). Seven ML models (Decision Tree, XGBoost, Random Forest, Logistic Regression, SVM, KNN, Naive Bayes) were trained and evaluated via stratified 5-fold cross-validation. Performance metrics included accuracy, precision, recall, F1 score, and AUC-ROC. Results : Decision Tree outperformed other models (accuracy: 99.45%, F1: 99.55%), identifying method duration (importance=0.35), provider type (0.25), and region (0.15) as top predictors. Regional disparities were stark (SNNP: 30.59% LARFP use vs. Amhara: 15.58%). Key reasons for method choice included fewer side effects (32.3%) and long duration (15.5%). Conclusion : Tree-based ML models effectively captured complex determinants of LARFP use. Targeted interventions addressing regional disparities, provider training, and client-centered care (e.g., reducing side effects) are critical for improving uptake.