TY  - JOUR
AU  - Gao, Jing
AU  - Jie, Xu
AU  - Yao, Yujun
AU  - Xue, Jingdong
AU  - Chen, Lei
AU  - Chen, Ruiyao
AU  - Chen, Jiayuan
AU  - Cheng, Weiwei
PY  - 2025
DA  - 2025/3/10
TI  - Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model
JO  - JMIR Pediatr Parent
SP  - e59377
VL  - 8
KW  - fetal birthweight
KW  - ensemble learning model
KW  - machine learning
KW  - prediction model
KW  - ultrasonography
KW  - macrosomia
KW  - low birth weight
KW  - birth weight
KW  - fetal
KW  - AI
KW  - artificial intelligence
KW  - prenatal
KW  - prenatal care
KW  - Shanghai
KW  - neonatal
KW  - maternal
KW  - parental
AB  - Background: Accurate third-trimester birth weight prediction is vital for reducing adverse outcomes, and machine learning (ML) offers superior precision over traditional ultrasound methods. Objective: This study aims to develop an ML model on the basis of clinical big data for accurate prediction of birth weight in the third trimester of pregnancy, which can help reduce adverse maternal and fetal outcomes. Methods: From January 1, 2018 to December 31, 2019, a retrospective cohort study involving 16,655 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. A total of 5 basic ML algorithms, including Ridge, SVM, Random Forest, extreme gradient boosting (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. International Peace Maternity and Child Health Hospital's Research Ethics Committee granted ethical approval for the usage of patient information (GKLW2021-20). Results: Train and test sets contained a total of 13,324 and 3331 cases, respectively. From a total of 59 variables, we selected 17 variables that were readily available for the “few feature model,” which achieved high predictive power with an accuracy of 81% and significantly exceeded ultrasound formula methods. In addition, our model maintained superior performance for low birth weight and macrosomic fetal populations. Conclusions: Our research investigated an innovative artificial intelligence model for predicting fetal birth weight and maximizing health care resource use. In the era of big data, our model improves maternal and fetal outcomes and promotes precision medicine. 
SN  - 2561-6722
UR  - https://pediatrics.jmir.org/2025/1/e59377
UR  - https://doi.org/10.2196/59377
DO  - 10.2196/59377
ID  - info:doi/10.2196/59377
ER  -