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

Author(s)

Victor, Audencio ; dos Santos, Hellen Geremias ; Silva, Gabriel Ferreira Santos ; Barcellos Filho, Fabiano ; Cobre, Alexandre de Fatima ; Luzia, Liania A. ; Rondo, Patricia H. C. ; Chiavegatto Filho, Alexandre Dias Porto

• Document type: Journal article
• Source: BMC PREGNANCY AND CHILDBIRTH; v. 24, n. 1, p. 11-pg., 2024-11-08.

Abstract

BackgroundGestational 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.MethodsWe analyzed data from the Araraquara Cohort, Brazil, which comprised 1557 pregnant women with a gestational age of 19 weeks or less. Predictors included socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors. Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. The models were trained and evaluated using a multiclass classification approach. Model performance was assessed using metrics such as area under the ROC curve (AUC-ROC), F1 score and Matthew's correlation coefficient (MCC).ResultsThe outcomes were categorized as follows: GWG within recommendations (28.7%), GWG below (32.5%), and GWG above recommendations (38.7%). The XGBoost presented the best overall model, achieving an AUC-ROC of 0.79 for GWG within, 0.76 for GWG below, and 0.65 for GWG above. The LightGBM also performed well with an AUC-ROC of 0.79 for predicting GWG within recommendations, 0.76 for GWG below, and 0.624 for GWG above. The most important predictors of GWG were pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference.ConclusionMachine learning models can effectively predict GWG categories, offering a valuable tool for early identification of at-risk pregnancies. This approach can enhance personalized prenatal care and interventions to promote optimal pregnancy outcomes. (AU)

• FAPESP's process: 23/07936-3 - The influence of gestational weight gain on fetal growth and neonatal outcomes: Araraquara Cohort Study.
• Grantee: Audencio Victor
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 15/03333-6 - The relationship between maternal adiposity and adiposity of the offspring in the fetal, neonatal and infant periods: a prospective population-based study
• Grantee: Patricia Helen de Carvalho Rondó
• Support Opportunities: Research Projects - Thematic Grants