Dental services use prediction among adults in Southern Brazil: A gender and racial fairness-oriented machine learning approach
Our findings highlight the potential of machine learning models to predict dental service use with good overall accuracy. However, the significantly lower performance for mixed-race individuals raises concerns about fairness and equity. Therefore, despite promising results, the model requires further refinement before it can be applied in real-world public health settings.
Authors
- Helena Silveira Schuch (ORCID: https://orcid.org/0000-0001-9932-9698)
- Felipe Mendes Delpino (ORCID: https://orcid.org/0000-0002-3562-3246)
- Luiz Alexandre Chisini (ORCID: https://orcid.org/0000-0002-3695-0361)
- 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)
- The University of Queensland (AU)
- Universidade Federal de Pelotas (BR)
- Radboud University Medical Center (NL)
- Alexion Pharma (Switzerland) (CH)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-06-21
- DOI
- https://doi.org/10.1016/j.jdent.2025.105929
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.93