Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms

Overview

Journal Life (Basel)

Specialty Biology

Date 2024 Nov 27

PMID 39598319

Authors

Seon Ui Lee

Sae Kyung Choi

Yun Sung Jo

Jeong Ha Wie

Jae Eun Shin

Yeon Hee Kim

Kicheol Kil

Hyun Sun Ko

Affiliations

Soon will be listed here.

Abstract

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.

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