Development and Validation of Machine Learning Models for Predicting Low Birth Weight in Singleton Pregnancies in Vietnam.

objectiveThis study compared seven machine learning (ML) algorithms to identify the most effective model for predicting low birth weight (LBW) in singleton pregnancies. The primary goal was to develop a high-accuracy screening tool to support clinical decision-making and early intervention.

methodsA prospective cross-sectional study was conducted among women delivering at the Women and Children Hospital of An Giang, Vietnam. Feature selection was performed using the Boruta algorithm, and data imbalance was addressed with the Synthetic Minority Over-sampling Technique (SMOTE). Seven ML algorithms - logistic regression (LR), random forest (RF), support vector machine, k-nearest neighbor, naïve Bayes, artificial neural network, and XGBoost (Seattle, WA: University of Washington) - were trained using five-fold cross-validation. Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity, while SHapley Additive exPlanations (SHAP) analysis was applied to interpret feature importance and explain the final model.

resultsAmong 1,838 women with singleton pregnancies (1,678 non-LBW and 160 LBW), the prevalence of LBW was 8.7% (95% CI: 7.5-10.0%). Of the seven ML models evaluated, the RF model achieved the highest overall performance, with an AUC of 0.778, an accuracy of 0.871, and a specificity of 0.909. LR demonstrated the highest sensitivity (0.581). SHAP analysis of the RF model identified preterm birth as the most important predictor of LBW, followed by primiparity, absence of gestational diabetes, abnormal cardiotocography (CTG) findings, pre-eclampsia, and a prior history of LBW.

conclusionML-based prediction of LBW enables early identification of high-risk pregnancies and enables timely preventive strategies. In this study, the RF model demonstrated the best predictive performance, with key predictors including preterm birth, primiparity (first-born status), absence of gestational diabetes, abnormal CTG finding, pre-eclampsia, and prior history of LBW. Early identification combined with appropriate perinatal and neonatal care may reduce infant mortality and severe morbidity.