Predictive modeling of gestational weight gain: a machine learning multiclass classification study.

Journal: BMC pregnancy and childbirth
Published Date: Nov 8, 2024

Abstract

BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines.

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.
  • Hellen Geremias Dos Santos
    University of São Paulo, Faculty of Public Health, São Paulo, São Paulo, Brazil.
  • Gabriel Ferreira Santos Silva
    School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil.
  • Fabiano Barcellos Filho
    School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil.
  • Alexandre de Fátima Cobre
    Department of Statistics, Federal University of Paraná, Curitiba, Paraná, Brazil.
  • Liania A Luzia
    School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil.
  • Patrícia H C Rondó
    School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil.
  • Alexandre Dias Porto Chiavegatto Filho
    From the Department of Epidemiology, School of Public Health of the University of Sao Paulo, Sao Paulo, SP, Brazil.

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