Machine learning-based prediction model for chronic brucellosis: a multi-feature approach using clinical and laboratory data
Using eight routinely available variables, the RF model demonstrated moderate discrimination with well-calibrated probability estimates but limited sensitivity. The tool may assist early risk stratification of chronic brucellosis when combined with clinical judgment; however, its predictive performance should be interpreted cautiously until validated in external, multicenter, and prospective studies.
Authors
- Chenming Zhang (ORCID: https://orcid.org/0000-0003-1426-0092)
- Liaoyun Zhang (ORCID: https://orcid.org/0000-0002-3682-0516)
- Rong Wang (ORCID: https://orcid.org/0000-0003-3994-3607)
- Xin Zhang (ORCID: https://orcid.org/0009-0000-9995-9112)
- Yinghan Wang (ORCID: https://orcid.org/0009-0002-5335-549X)
- Haiyan Tian
- Bin Niu
Institutions
- Shanxi Medical University (CN)
- First Hospital of Shanxi Medical University (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-11-19
- DOI
- https://doi.org/10.3389/fcimb.2025.1700233
- Citations
- 2
- Primary Topic
- Brucella: diagnosis, epidemiology, treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.71