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

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: BMC PREGNANCY AND CHILDBIRTH; v. 24, n. 1, p. 11-pg., 2024-11-08.

Resumo

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)

• Processo FAPESP: 23/07936-3 - A influência do ganho de peso gestacional no crescimento fetal e nos desfechos neonatais: Estudo Coorte Araraquara
• Beneficiário: Audencio Victor
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 15/03333-6 - Relação entre adiposidade materna e adiposidade do concepto nos períodos fetal, neonatal e no primeiro ano de vida: estudo prospectivo de base populacional
• Beneficiário: Patricia Helen de Carvalho Rondó
• Modalidade de apoio: Auxílio à Pesquisa - Temático