Using machine learning to predict the small for gestational age and identify the important predictors: A real-world clinical cohort study in China

Purpose: Aims to use machine learning to predict the risk of small for gestational age (SGA) and identify its important predictors. Methods: This is a retrospective cohort study conducted from December 20, 2023, to May 20, 2024, focusing on newborns and their mothers who delivered at Peking University People’s Hospital from January 1, 2012, to December 31, 2022. We included a total of 18,164 pregnant women. We adopted 7 machine-learning-based models (2 linear models, 4 tree-based models, and 1 ensemble learning model). Results: Altogether, 1437 (7.9%) pregnant women delivered SGA births. Among them, 27.7% and 72.3% were moderate-to-severe and mild types of SGA, respectively, and the percentages of term and preterm SGA were 88.1% and 11.9%, respectively. Although the ridge classifier (linear-based model) performed better than the other 6 models in terms of model discrimination (AUROC: 0.71), the performance of all 7 models in calibration remained unsatisfactory. All of them tended to underestimate the risk of SGA and could not capture approximately half of the SGA births (recall: 0.49). Maternal height was shown as the most important predictor for the SGA, moderate-to-severe SGA, full-term SGA, and preterm SGA, even outweighing the predictors of pre-pregnancy BMI and gestational weight gain. For mothers shorter than 158 cm, their risk of delivering SGA births was 3.61 (95% CI: 2.91 to 4.50) per 1-SD decrease in height, but for those higher than 158 cm, the SGA risk was shown no evidence of association with maternal height (P > 0.05). Conclusions: Our study not only contributes a basic model for the prediction of SGA, but also identified the short maternal height as a previously neglected predictor of SGA.