{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Victor A"],"funding":["Fundação de Amparo à Pesquisa do Estado de São Paulo"],"pagination":["733"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11549867"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>We 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"],"journal":["BMC pregnancy and childbirth"],"pubmed_title":["Predictive modeling of gestational weight gain: a machine learning multiclass classification study."],"pmcid":["PMC11549867"],"funding_grant_id":["2015/03333-6","2023/07936-3"],"pubmed_authors":["Chiavegatto Filho ADP","Victor A","de Fatima Cobre A","Geremias Dos Santos H","Barcellos Filho F","Luzia LA","Silva GFS","Rondo PHC"],"additional_accession":[]},"is_claimable":false,"name":"Predictive modeling of gestational weight gain: a machine learning multiclass classification study.","description":"<h4>Background</h4>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.<h4>Methods</h4>We 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","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-07-16T19:54:53.987Z","creation":"2025-04-04T18:55:41.44Z"},"accession":"S-EPMC11549867","cross_references":{"pubmed":["39516752"],"doi":["10.1186/s12884-024-06952-8"]}}