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Updated: Jun 6, 2025
Fetal Echocardiography and Pulsed-wave Doppler Ultrasound in a Rabbit Model of Intrauterine Growth Restriction
Published on: June 29, 2013
Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms
Seon Ui Lee1, Sae Kyung Choi1, Yun Sung Jo2
1Department of Obstetrics and Gynecology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Background:
This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR).
Methods:
This retrospective study included seven hospitals and was conducted between January 2009 and December 2020. Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. The dataset was randomly divided into training and test sets (7:3 ratio). A simplified prediction model using variables with XGBoost's embedded feature selection was developed and validated.
Results:
Precisely 32,301 patients met the eligibility criteria. In the prediction model for the whole cohort, the area under the curve (AUC) was 0.73 at E1 and 0.78 at T1 and the area under the precision-recall curve (AUPR) was 0.23 at E1 and 0.31 at T1 in the training set, while an AUC of 0.62 at E1 and 0.73 at T1 and an AUPR if 0.13 at E1, and 0.24 at T1 were obtained in the test set. The simplified prediction model performed similarly to the original model.
Conclusions:
A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester.
