Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study

Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study

Abstract Background Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost,...

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Bibliographic Details
Main Authors: Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, Patrícia H.C. Rondó
Format: Article
Language:English
Published: BMC 2025-03-01
Series:BMC Pregnancy and Childbirth
Subjects:
Low birth weight
Machine learning
XGBoost
Random forest
Araraquara cohort
Online Access:https://doi.org/10.1186/s12884-025-07351-3
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