AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors
Machine learning models, particularly tree-based ensemble methods, offer robust tools for diabetes risk prediction. These findings support their integration into public health analytics for personalized risk assessment and data-driven prevention strategies.
Authors
- Paweł Sobczak (ORCID: https://orcid.org/0000-0003-2167-1498)
- Dawid Majcherek (ORCID: https://orcid.org/0000-0003-4352-5643)
- Adam Ciesielski (ORCID: https://orcid.org/0000-0001-9015-8204)
Institutions
- SGH Warsaw School of Economics (PL)
- Konya Technical University (TR)
- Lubawa (Poland) (PL)
- Uniwersytet Kaliski im. Prezydenta Stanisława Wojciechowskiego (PL)
- State University of Applied Sciences in Konin (PL)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-09-03
- DOI
- https://doi.org/10.1371/journal.pone.0328655
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Uddannelses- og Forskningsministeriet