Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants.

ABSTRACT: This study aimed to analyze major predictors of adverse birth outcomes in very low birth weight (VLBW) infants including particulate matter concentration (PM10), using machine learning and the national prospective cohort. Data consisted of 10,423 VLBW infants from the Korean Neonatal Network database during January 2013-December 2017. Five adverse birth outcomes were considered as the dependent variables, i.e., gestational age less than 28 weeks, gestational age less than 26 weeks, birth weight less than 1000 g, birth weight less than 750 g and small-for-gestational age. Thirty-three predictors were included and the artificial neural network, the decision tree, the logistic regression, the Naïve Bayes, the random forest and the support vector machine were used for predicting the dependent

SUBMITTER: Cho H 

PROVIDER: S-EPMC9526718 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature