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July 17, 2026Open Access

Determinants of modern family planning use among married adolescents in Bangladesh: evidence from a machine learning approach

SIShawkatul IslamJahangirnagar University MEMd Eyah EyaJahangirnagar University MAMuhammad Khairul AlamJahangirnagar University

Key Points

This study aims to identify determinants of modern family planning use among married adolescents in Bangladesh using machine learning techniques.
Analyzed data from the Bangladesh Adolescent Health and Wellbeing Survey 2019–20 with 3,223 adolescents aged 15 to 19.
Employed machine learning models including XGBoost, Random Forest, and Neural Network for prediction and feature selection.
Evaluated model performance using metrics like accuracy, precision, recall, and AUROC.
72.66% of married adolescents reported using a modern family planning method.
XGBoost achieved 76.0% accuracy, 81.5% precision, and 86.5% recall after class balancing.
Key determinants included spousal co-residency, childbirth status, decision-making autonomy, and household wealth.

Abstract

Modern family planning (FP) is essential for improving the reproductive health of adolescent women, especially in Bangladesh, where early marriage is still prevalent. Despite overall progress in family planning, modern FP use remains uneven and relatively low among married adolescent women due to social, relational, and contextual barriers. Conventional statistical approaches might not be able to fully capture intricate and non-linear determinants of FP use. Therefore, this study applied and compared multiple machine learning (ML) models to predict modern FP use and identify its key determinants among married adolescent women in Bangladesh. Data were obtained from the nationally representative Bangladesh Adolescent Health and Wellbeing Survey (BAHWS) 2019–20. The analysis included a weighted sample of 3,223 ever-married adolescent females aged 15 to 19 years. A variety of machine learning classification models were employed, including Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Network (NN), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Categorical Naïve Bayes (CNB). Feature selection was performed with the Boruta algorithm, and model interpretability was examined through SHapley Additive exPlanations (SHAP). Model performance was assessed using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient, Cohen’s Kappa, and area under the receiver operating characteristic curve (AUROC). Overall, 72.66% of married adolescent women reported using a modern FP method. Ensemble-based models outperformed conventional classifiers, especially after class balancing. XGB had the best overall predictive performance after applying SMOTE, with 76.0% accuracy, 81.5% precision, 83.9% F1 Score, 37.2% MCC, 36.9% Cohen’s Kappa, 86.5% recall, and an AUROC of 72.9%, followed by RF and NN models. The most important determinants of modern FP use included spousal co-residency, having given birth, joint decision-making about FP use, administrative division, younger age (15–17), household wealth, and media exposure. SHAP interaction analysis further revealed important non-linear relationships, particularly between childbirth status and spousal co-residency, decision-making autonomy and household wealth, and media exposure and geographic division, highlighting substantial contextual heterogeneity in modern FP use. This study reveals that machine learning models, especially XGBoost, show potential for predicting modern family planning use and identifying key determinants among married adolescent women in Bangladesh. The findings provide useful insights for policymakers and program planners to create adolescent-focused family planning programs. The results highlight the significance of regional disparities, socioeconomic inequities, and relationship factors in influencing adolescent contraceptive behavior. Integrating ML-based evidence into family planning programs may enhance targeted interventions and help to achieve Sustainable Development Goal 3.7.

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