Predicting negative self-rated oral health in adults using machine learning: A longitudinal study in Southern Brazil
Our findings highlighted the potential of using machine learning to predict subjective oral health conditions. This model should be improved to make it feasible for real-world implementation, considering its limitations. The correct identification of individuals with negative self-rated oral health may support the development of targeted strategies focused on these at-risk groups.
Authors
- Helena Silveira Schuch (ORCID: https://orcid.org/0000-0001-9932-9698)
- Felipe Mendes Delpino (ORCID: https://orcid.org/0000-0002-3562-3246)
- Alexandre Dias Porto Chiavegatto Filho (ORCID: https://orcid.org/0000-0003-3251-9600)
- Lílian Munhoz Figueiredo (ORCID: https://orcid.org/0000-0001-5152-5059)
- Bruno Pereira Nunes (ORCID: https://orcid.org/0000-0002-4496-4122)
- Flávio Fernando Demarco (ORCID: https://orcid.org/0000-0003-2276-491X)
- Cinthia Fonseca Araujo (ORCID: https://orcid.org/0000-0002-5148-4274)
Institutions
- Radboud University Nijmegen (NL)
- University of Illinois Urbana-Champaign (US)
- Universidade Federal de Pelotas (BR)
- Universidade de São Paulo (BR)
- Radboud University Medical Center (NL)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1016/j.jdent.2025.106164
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00