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Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms

Authors :
Seon Ui Lee
Sae Kyung Choi
Yun Sung Jo
Jeong Ha Wie
Jae Eun Shin
Yeon Hee Kim
Kicheol Kil
Hyun Sun Ko
Source :
Life, Vol 14, Iss 11, p 1521 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

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.

Details

Language :
English
ISSN :
20751729
Volume :
14
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Life
Publication Type :
Academic Journal
Accession number :
edsdoj.22cce66ed4b2198a28a1c5f3b9f3e
Document Type :
article
Full Text :
https://doi.org/10.3390/life14111521