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Background <p>Low birth weight (LBW) remains a leading cause of neonatal mortality and long-term morbidity in low- and middle-income countries. This study aimed to develop and evaluate machine learning classifiers for predicting LBW using nationally representative survey data from Bangladesh, while explicitly distinguishing predictive modeling from causal inference.</p> Methods <p>We analyzed data from the 2022 Bangladesh Demographic and Health Survey (BDHS), yielding a final analytic sample of 3,400 mother–child pairs after complete-case exclusion. Survey weights, stratification, and clustering were incorporated into all modeling steps via sample_weight parameters and cluster-aware data splitting. Class imbalance was addressed using native class-weight optimization (scale_pos_weight, class_weight = "balanced") rather than synthetic oversampling to preserve survey representativeness. Seven machine learning classifiers were evaluated under a cluster-aware train-validation-test split. Model performance was assessed using discrimination metrics (AUROC, PR-AUC), calibration metrics (Brier score, slope, intercept), and 95% confidence intervals derived via stratified bootstrap resampling (B = 1,000). SHapley Additive exPlanations (SHAP) were used for model interpretability, with explicit framing of findings within a predictive context.</p> Results <p>XGBoost demonstrated the best calibrated and discriminative performance on the independent test set: AUROC = 0.828 (95% CI: 0.764–0.887), sensitivity = 0.711 (0.600–0.816), specificity = 0.847 (0.814–0.876), Brier score = 0.095 (0.077–0.114). SHAP analysis identified geographical division, birth order, paternal education, and household wealth as the most influential predictors. Variables non-significant in bivariate analysis but influential in XGBoost (e.g., child's sex, maternal age) likely contribute through higher-order interactions captured by tree-based ensembles. The positive association between antenatal care visits and predicted LBW risk likely reflects clinical triage patterns rather than causal harm.</p> Conclusions <p>Survey-aware machine learning, particularly XGBoost, provides a robust framework for LBW risk stratification in Bangladesh. While observational design precludes causal inference and external validation remains necessary, these findings support the potential utility of interpretable ML models for informing targeted maternal health interventions. Future work should prioritize prospective validation and incorporation of clinical biomarkers.</p>

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Predicting low birth weight in Bangladesh using interpretable machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Samrat Kumar Dev Sharma,
  • Md. Yusuf Hossain Ador,
  • Md. Rukonuzzaman,
  • Futanta Chakma,
  • Mahmud Hossen,
  • Jakir Hossain,
  • Md. Kamruzzaman

摘要

Background

Low birth weight (LBW) remains a leading cause of neonatal mortality and long-term morbidity in low- and middle-income countries. This study aimed to develop and evaluate machine learning classifiers for predicting LBW using nationally representative survey data from Bangladesh, while explicitly distinguishing predictive modeling from causal inference.

Methods

We analyzed data from the 2022 Bangladesh Demographic and Health Survey (BDHS), yielding a final analytic sample of 3,400 mother–child pairs after complete-case exclusion. Survey weights, stratification, and clustering were incorporated into all modeling steps via sample_weight parameters and cluster-aware data splitting. Class imbalance was addressed using native class-weight optimization (scale_pos_weight, class_weight = "balanced") rather than synthetic oversampling to preserve survey representativeness. Seven machine learning classifiers were evaluated under a cluster-aware train-validation-test split. Model performance was assessed using discrimination metrics (AUROC, PR-AUC), calibration metrics (Brier score, slope, intercept), and 95% confidence intervals derived via stratified bootstrap resampling (B = 1,000). SHapley Additive exPlanations (SHAP) were used for model interpretability, with explicit framing of findings within a predictive context.

Results

XGBoost demonstrated the best calibrated and discriminative performance on the independent test set: AUROC = 0.828 (95% CI: 0.764–0.887), sensitivity = 0.711 (0.600–0.816), specificity = 0.847 (0.814–0.876), Brier score = 0.095 (0.077–0.114). SHAP analysis identified geographical division, birth order, paternal education, and household wealth as the most influential predictors. Variables non-significant in bivariate analysis but influential in XGBoost (e.g., child's sex, maternal age) likely contribute through higher-order interactions captured by tree-based ensembles. The positive association between antenatal care visits and predicted LBW risk likely reflects clinical triage patterns rather than causal harm.

Conclusions

Survey-aware machine learning, particularly XGBoost, provides a robust framework for LBW risk stratification in Bangladesh. While observational design precludes causal inference and external validation remains necessary, these findings support the potential utility of interpretable ML models for informing targeted maternal health interventions. Future work should prioritize prospective validation and incorporation of clinical biomarkers.