Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms.

ABSTRACT:

Background

Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy.

Methods

Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same-sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data.

Results

A total of 139 newborns were diagnosed as

SUBMITTER: Wang N 

PROVIDER: S-EPMC10101839 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature