Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.

ABSTRACT:

Background

Predicting birth weight and identifying its risk factors are clinically important. This study aims to use interpretable machine learning to predict birth weight and identity important predictors.

Methods

This prospective cohort study was conducted in Tongzhou Maternal and Child Health Care Hospital of Beijing, China, recruiting pregnant women between June 2018 and February 2019. We used 24 features to predict infant birth weight, including gestational age, mother's age, parity, history of macrosomia delivery, pre-pregnancy body mass index (BMI), height, father's BMI, lifestyle (diet, physical activity, smoking), and biomarker (fasting glucose and lipids) features. Study outcome was birth weight of infant. We used 8 supervised learning models including 4 individual

SUBMITTER: Liu Z 

PROVIDER: S-EPMC9691849 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature