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

Audêncio Victor1, Hellen Geremias Dos Santos2, Gabriel Ferreira Santos Silva3

  • 1School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil. audenciovictor@gmail.com.

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|November 8, 2024
PubMed
Summary

Machine learning accurately predicts gestational weight gain (GWG) categories, identifying at-risk pregnancies early. This aids personalized prenatal care for better maternal and fetal health outcomes.

Keywords:
Araraquara cohortFetal healthGestational weight gainMachine learningMaternal healthPrediction models

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Area of Science:

  • Maternal-fetal medicine
  • Computational biology
  • Public health

Background:

  • Gestational weight gain (GWG) significantly impacts maternal and fetal health.
  • Deviations from recommended GWG are linked to complications like gestational diabetes, hypertension, and adverse birth outcomes.
  • Predicting GWG categories is crucial for timely interventions.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting GWG categories (below, within, or above recommended guidelines).
  • To identify key predictors influencing gestational weight gain.
  • To assess the utility of ML in enhancing prenatal care for optimal pregnancy outcomes.

Main Methods:

  • Analysis of data from 1557 pregnant women in the Araraquara Cohort, Brazil.
  • Utilized socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors as predictors.
  • Employed and compared five ML algorithms: Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost for multiclass classification.

Main Results:

  • The XGBoost model demonstrated the highest performance with an AUC-ROC of 0.79 for GWG within recommendations.
  • Key predictors identified include pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference.
  • The distribution of GWG categories was: within (28.7%), below (32.5%), and above (38.7%) recommendations.

Conclusions:

  • ML models offer a robust method for predicting GWG categories, enabling early identification of pregnancies at risk.
  • This predictive capability supports personalized prenatal care and targeted interventions.
  • The study highlights the potential of ML to improve maternal and fetal health outcomes through enhanced pregnancy management.