Assessment of socioeconomic and demographic risk factors for low birth weight using model-agnostic explainable...

Low birth weight (LBW) is a major global public health concern, strongly linked to neonatal morbidity and long-term health complications. Early prediction of LBW is essential to reduce neonatal mortality and guide targeted healthcare interventions. This study proposes a predictive framework integrating machine learning (ML), deep learning (DL), and model-agnostic eXplainable Artificial Intelligence (XAI) to identify key socioeconomic and demographic determinants of LBW.

Data from the Bangladesh Demographic and Health Survey (BDHS), comprising 1574 participants and 12 variables, are analyzed. Key predictors included maternal age, education, household wealth, geographic region, birth order, and maternal BMI. Chi-square tests assess variable associations. A stacking ensemble model, SmartFusion-LR5, is developed, combining K-Nearest Neighbors, Logistic Regression (LR), Decision Tree, Random Forest, and Naive Bayes, with LR as the meta-learner. Model performance is evaluated using accuracy, precision, recall, area under the curve (AUC), F1-score, and Matthews correlation coefficient (MCC).

Significant disparities in LBW prevalence are observed across geographic divisions, with higher parental education and socioeconomic status associated with healthier outcomes. The SmartFusion-LR5 model achieves the highest overall discriminative capability compared to baselines, attaining 93.0% accuracy, 86.7% precision, 99.8% recall, 92.8% F1-score, 94.0% AUC, and an MCC of 86.0%. Comparable performance also obtained from SmartFusion-XGB4 (91.8% accuracy, 86.2% precision, 99.6% recall, 92.4% F1-score, 92.2% AUC, MCC 84.8%) and SmartFusion-RF4 (91.7% accuracy, 86.2% precision, 99.2% recall, 92.2% F1-score, 92.0% AUC, MCC 84.4%). Global XAI methods identified age at first birth, division, residence, wealth, and husband's education as key determinants, while local explanations revealed individual feature impacts.

The proposed framework offers a robust, interpretable, and scalable approach for early LBW risk prediction, supporting targeted maternal and child health interventions in resource-constrained settings.