Predictive modeling of gestational weight gain: a machine learning multiclass classification study
Machine 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.
Authors
- Audêncio Victor (ORCID: https://orcid.org/0000-0002-8161-3639)
- Alexandre Dias Porto Chiavegatto Filho (ORCID: https://orcid.org/0000-0003-3251-9600)
- Patrícia Helen de Carvalho Rondó (ORCID: https://orcid.org/0000-0002-8308-6393)
- Liânia Alves Luzia (ORCID: https://orcid.org/0000-0001-8317-1398)
- Fabiano Barcellos Filho (ORCID: https://orcid.org/0000-0002-1491-2700)
- Alexandre de Fátima Cobre (ORCID: https://orcid.org/0000-0001-6642-3928)
- Gabriel Silva (ORCID: https://orcid.org/0000-0002-5660-9769)
- Hellen Geremias dos Santos (ORCID: https://orcid.org/0000-0001-7039-8585)
Institutions
- Universidade de São Paulo (BR)
- Fundação Carlos Chagas (BR)
- Fundação Oswaldo Cruz (BR)
- Universidade Federal do Paraná (BR)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1186/s12884-024-06952-8
- Citations
- 4
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 2.33
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo