Uncovering the determinants of high-risk age at childbirth in Bangladesh: A machine learning analysis of the BDHS 2022 data

A T M Ahsanul Kyum Siam, Tanzila Tanjim Tusra and Md Abul Hossain

PLOS ONE, 2026, vol. 21, issue 8, 1-23

Abstract: Childbirth occurring at high-risk maternal ages (≤18 years or ≥40 years) remains an important public health concern in Bangladesh and is influenced by a range of socio-demographic and behavioral factors. We conducted a cross-sectional secondary analysis of 15,386 ever married women from the BDHS 2022 dataset. After cleaning and removing duplicates and missing values, data were split into training 80 percent and testing 20 percent sets. Three feature selection methods Lasso with 12 features, Chi Square with 13 features, and Boruta with 10 features were applied. Machine learning models including non-ensemble models CART, SVM, KNN, Naive Bayes and ensemble models Random Forest, GBM, AdaBoost, XGB were trained using 10-fold stratified cross validation with SMOTE applied to balance classes. Model performance was evaluated using accuracy, precision, recall, F1 score, Area Under the Receiver Operating Characteristic Curve (AUROC), and Area Under the Precision-Recall Curve (AUPRC).. Among the evaluated machine learning models, CART achieved the best overall performance for the Lasso-selected features (Accuracy = 0.77, F1-score = 0.69, AUROC = 0.74, AUPRC = 0.54), while GBM performed best for the Chi-square-selected features (Accuracy = 0.80, F1-score = 0.71, AUROC = 0.79, AUPRC = 0.64) and Boruta-selected features (Accuracy = 0.75, F1-score = 0.64, AUROC = 0.69). Overall, the GBM model with Chi-square-selected features demonstrated the strongest predictive performance, achieving the highest AUROC (0.79) and F1-score (0.71). Key predictors of high-risk age at childbirth included maternal age, early marriage, current contraceptive use, husband occupation, number of children, husband age and education, respondent age, and regional disparities. Findings were consistent with prior South Asian and LMIC studies while highlighting the added relevance of partner education and media exposure. Machine learning approaches identified the most influential socio demographic and behavioral determinants of high-risk age at childbirth in Bangladesh. The results provide an evidence-based framework to guide policymakers in designing interventions such as delaying age at first birth, expanding female education, and targeting high risk districts to improve maternal and child health outcomes.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0356362

DOI: 10.1371/journal.pone.0356362

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