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

Journal: BMC pregnancy and childbirth
PMID: 40108493

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, Catboost, and LightGBM.

Authors

  • Audêncio Victor
    School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil. audenciovictor@gmail.com.
  • Francielly Almeida
    Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto, FEA-RP/USP, Ribeirão Preto, São Paulo, Brazil.
  • Sancho Pedro Xavier
    Institute of Collective Health, Federal University of Mato Grosso. Cuiabá, Mato Grosso, Brazil.
  • Patrícia H C Rondó
    School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil.

Keywords

External Resources

Popular Topics
Recent Journals