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

Machine learning, combined with SMOTE, proved to be an effective approach for predicting LBW. XGBoost stood out as the most accurate model, but Catboost and Random Forest also provided solid results. These models can be applied to identify high-risk pregnancies, improving perinatal outcomes through early interventions.

Authors

Audêncio Victor (ORCID: https://orcid.org/0000-0002-8161-3639)
Sancho Pedro Xavier (ORCID: https://orcid.org/0000-0001-9493-4098)
Patrícia Helen de Carvalho Rondó (ORCID: https://orcid.org/0000-0002-8308-6393)
Francielly Almeida

Institutions

Universidade de São Paulo (BR)
Universidade Federal de Mato Grosso (BR)
Instituto de Saúde (BR)
Fundace (BR)
Ministry of Health (MZ)

Publication Details

Journal: BMC Pregnancy and Childbirth
Published: 2025-03-19
DOI: https://doi.org/10.1186/s12884-025-07351-3
Citations: 4
Primary Topic: Maternal and Neonatal Healthcare
Type: article
Field-Weighted Citation Impact: 11.26

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article

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

Audêncio Victor, Sancho Pedro Xavier, Patrícia Helen de Carvalho Rondó, Francielly Almeida

Mar 19, 2025

4 citations

BMC Pregnancy and Childbirth

Maternal and Neonatal Healthcare

11.26

article

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

Audêncio Victor, Sancho Pedro Xavier, Patrícia Helen de Carvalho Rondó, Francielly Almeida

March 19, 2025article en

4 citations

Abstract

Machine learning, combined with SMOTE, proved to be an effective approach for predicting LBW. XGBoost stood out as the most accurate model, but Catboost and Random Forest also provided solid results. These models can be applied to identify high-risk pregnancies, improving perinatal outcomes through early interventions.

BMC Pregnancy and ChildbirthVol. 25(1)

Universidade de São Paulo (BR), Universidade Federal de Mato Grosso (BR), Instituto de Saúde (BR), Fundace (BR), Ministry of Health (MZ)

Gender equality

Openalex Percentile: Top 2%

Maternal and Neonatal Healthcare

11.26

DOI Open Access PDF OpenAlex

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Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study — Audêncio Victor, Sancho Pedro Xavier, et al. · BMC Pregnancy and Childbirth (2025) | TGRS Research Map | TGRS