Utilization of tree-based machine learning models for predicting low birth weight cases
The conclusions of this study indicate that the removal of duplicate data and careful attribute selection positively influenced the performance of the machine learning models in predicting low birth weight. Additionally, the analysis of attribute importance highlighted socio-demographic characteristics and gestational history as the most influential in the training of the models.
Authors
- Gabriel Masson (ORCID: https://orcid.org/0000-0003-3242-8119)
- Flávio Leandro Morais (ORCID: https://orcid.org/0000-0001-9218-2523)
- Raphael A. Dourado
- Patrícia Takako Endo (ORCID: https://orcid.org/0000-0002-9163-5583)
- Waldemar Brandão Neto (ORCID: https://orcid.org/0000-0003-4786-9961)
- Djalma Manoel Soares Filho (ORCID: https://orcid.org/0000-0002-7541-9899)
- Élisson da Silva Rocha (ORCID: https://orcid.org/0000-0002-7742-2995)
- Kátia Maria Mendes
Institutions
- Universidade Federal de Pernambuco (BR)
- Universidade Federal de Campina Grande (BR)
- Universidade Federal Rural de Pernambuco (BR)
- Universidade de Pernambuco (BR)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1186/s12884-025-07303-x
- Citations
- 1
- Primary Topic
- Neonatal and fetal brain pathology
- Type
- article
- Field-Weighted Citation Impact
- 3.49
Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico