<![CDATA[Background: Repeat adolescent pregnancy compounds obstetric and socioeconomic risk, yet conventional models seldom capture the non-linear interplay of psychosocial and sociodemographic determinants, particularly in low- and middle-income registries where such variables are rarely recorded. Objective: To develop and compare interpretable machine-learning models to identify determinants of repeat adolescent pregnancy in an Indonesian metropolitan cohort. Methods: In this retrospective cohort study, electronic medical records (2018–2023) from a tertiary referral hospital and affiliated primary-care network in Palembang, Indonesia, were analysed for 1,245 adolescents (aged 10–19 years) with at least one prior pregnancy. The outcome was a second pregnancy before age 20. Five models (logistic regression, random forest, support vector machine, multilayer perceptron, and XGBoost) were trained with SMOTE and 5-fold cross-validation; discrimination was assessed by AUC-ROC and interpretability by SHAP. A multivariable logistic model provided adjusted odds ratios (aOR). Results: Repeat pregnancy occurred in 312/1,245 adolescents (25.06%; 95% CI 22.7–27.5%). XGBoost achieved the highest discrimination (AUC 0.89, 95% CI 0.86–0.92; F1 0.84). Independent determinants were non-use of postpartum LARC (aOR 8.86, 95% CI 6.00–13.07; p<0.001), low family support (aOR 3.82; p<0.001), education at most junior high (aOR 3.76; p<0.001), elevated EPDS (aOR 2.92, 95% CI 1.95–4.37; p<0.001), and age under 16 at first pregnancy (aOR 2.85; p<0.001). Postpartum LARC was strongly preventive (NNT approximately 3). Conclusion: Interpretable gradient boosting accurately stratified repeat adolescent pregnancy risk, and psychosocial determinants carried predictive weight comparable to contraceptive non-use. These findings support risk-stratified, bio-psycho-social postpartum care and targeted LARC counselling for adolescent mothers in Indonesia.]]>
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