Abstract:

Objective: To compare the performance of multiple machine learning algorithms in developing predictive models for neonatal hypoglycemia (NH), with the aim of early identification of high-risk pregnant women likely to deliver neonates with hypoglycemia. Methods: A retrospective analysis was conducted on 286 pregnant women admitted to Shanxi Children's Hospital from January 2021 to July 2025. Participants were randomly allocated into a training set (n=200) and a test set (n=86) at a 7:3 ratio. The primary outcome measure was the occurrence of NH. Feature selection was performed using univariate analysis, LASSO regression, and the Boruta algorithm. Predictive models were constructed based on the selected features, and Bootstrap cross-validation was applied for model validation. Results: Through univariate analysis, LASSO regression, and Boruta feature selection, pre-pregnancy body mass index, gestational weight gain,mode of delivery,gestational diabetes mellitus, hypertensive disorders of pregnancy, antenatal corticosteroid therapy for fetal lung maturation, neonatal birth weight,and preterm birth were identified as influencing factors for NH (P<0.05). In the training set, the AUC values for the random forest, decision tree, support vector machine, and Logistic regression models were 0.967, 0.943, 0.800, and 0.955, respectively; the corresponding values in the test set were 0.921, 0.860, 0.882, and 0.910, respectively. A comprehensive comparison of performance metrics across both the training and test sets demonstrated that the random forest model achieved superior overall performance. Conclusions: The random forest model can be applied to predict the risk of NH occurrence and demonstrates considerable clinical utility.

Key words: Hypoglycemia, Infant, newborn, Machine learning, Predictive model, Pregnancy management