(1058) Development and validation of ensemble model for fetal birthweight prediction in third-trimester pregnant women

Objective: On the basis of clinical big data, we aim to develop a machine learning (ML) model for accurate prediction of birth weight in the third trimester of pregnancy, which can help reduce adverse maternal and fetal outcomes.

Study Design: From 1 January 2018 to 31 December 2019, a retrospective cohort study involving 16655 singleton live births without congenital anomalies ( > 28 weeks of gestation) was conducted in a tertiary first-class hospital in Shanghai. The initial set of data was divided into a train set for algorithm development and a test set on which the algorithm was divided in a ratio of 4:1. We extracted maternal and neonatal delivery outcomes, as well as parental demographics, obstetric clinical data, and sonographic fetal biometry, from electronic medical records. Five basic machine learning algorithms, including Ridge, SVM, Random Forest, XGBoost, and Multi-Layer Perceptron, were used to develop the prediction model, which was then averaged into an ensemble learning model. The models were compared using accuracy, mean squared error, root mean squared error, and mean absolute error.

Results: Train and test sets contained a total of 13324 and 3331 cases, respectively. From a total of 59 variables, we selected 17 variables that were readily available for the "few feature model." which achieve high predictive power with an accuracy of 81.84% and significantly exceeds ultrasound formula methods. In addition, our model maintained superior performance for low birth weight and macrosomic fetal populations.

Conclusion: Our research investigated an innovative artificial intelligence model for predicting fetal birthweight and maximizing healthcare resource utilization. In the era of big data, our model improves maternal and fetal outcomes and promotes precision medicine.