Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study.

ABSTRACT:

Background

This study introduced machine learning approaches to predict newborn's body mass index (BMI) based on ultrasound measures and maternal/delivery information.

Methods

Data came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn's BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later.

Results

Based on variable importance from the random forest, major predictors of newbo

SUBMITTER: Lee KS 

PROVIDER: S-EPMC7927215 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature