Antenatal prediction of small for gestational age at birth based on four birthweight standards using machine learning algorithms.

Background: Accurate antenatal prediction of SGA at birth is essential to improve development and delivery of preventative and therapeutic interventions. This study aimed to assess the performance of machine learning (ML) models to predict SGA at birth among Chinese pregnancies classified according to the Chinese birthweight standard and three international birthweight standards. Methods: We collected multimodal, longitudinal, antenatal surveillance data on 350,135 singleton pregnancies in Wenzhou City, China, between Jan 1, 2014 and Dec 31, 2016. For three pregnancy intervals we developed ML prediction models for newborns classified as SGA using the China, Intergrowth 21st, Fetal Medicine Foundation (FMF), and Gestation-related Optimal Weight (GROW) standards. We applied lasso regression to conduct feature selection, and CatBoost, XGBoost, LightBoost, Artificial Neural Networks, Random Forest, Stacked ensemble model, and logistic regression for predictive modeling in training data sets, with validation in testing data sets. Results: Among 22,603 singleton pregnancies with complete data, the rate of SGA using the China standard was 6.1%, compared to 4.3, 6.0, and 9.7% for the Intergrowth 21st, GROW, and FMF standards, respectively. This pattern was maintained in the imputed data set ( Conclusion: There are important differences in the classification of SGA at birth between national and international birthweight standards. Both machine learning models and traditional logistic regression demonstrated comparable predictive performance for SGA identification. These findings hold promise for guiding risk-stratified prenatal care and optimizing resource allocation in clinical settings.