Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study
Soft drinks, filled cookies, and chips were key dietary predictors of elevated blood pressure, with higher intake in adolescents. Machine learning with transfer learning effectively identified these risks, emphasizing the need for early dietary interventions to prevent hypertension and support cardiovascular health in pediatric populations.
Authors
- Mateus Silva Rocha
- Alexandre Dias Porto Chiavegatto Filho (ORCID: https://orcid.org/0000-0003-3251-9600)
- Augusto César Ferreira de Moraes (ORCID: https://orcid.org/0000-0002-5763-435X)
- Keisyanne Araújo‐Moura (ORCID: https://orcid.org/0000-0003-1022-1950)
- Tiago Almeida de Oliveira (ORCID: https://orcid.org/0000-0002-1276-1398)
- Letícia Souza (ORCID: https://orcid.org/0000-0001-6758-1645)
Institutions
- Universidade Estadual da Paraíba (BR)
- Universidade de São Paulo (BR)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- International Journal of Public Health
- Published
- 2025-03-11
- DOI
- https://doi.org/10.3389/ijph.2025.1607944
- Citations
- 2
- Primary Topic
- Blood Pressure and Hypertension Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.78
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Conselho Nacional de Desenvolvimento Científico e Tecnológico