Identification of risk factors for latent tuberculosis infection in Xinjiang using machine learning
This study successfully identified key LTBI risk factors using machine learning approaches. The developed nomogram provides a practical tool for targeted screening in resource-limited settings. Interventions targeting modifiable factors such as smoking cessation and occupational dust control may reduce LTBI and active TB burden.
Authors
- Yu Tzu Wu (ORCID: https://orcid.org/0000-0002-2623-2526)
- Yanjie Wang (ORCID: https://orcid.org/0000-0002-0475-3769)
- Yang Xiang
- Zhen Luo (ORCID: https://orcid.org/0009-0006-7815-7464)
- Xiaojing Wen (ORCID: https://orcid.org/0000-0001-8482-7176)
- Mairihaba kamili
- LiTing Yuan
Institutions
- Xinjiang Medical University (CN)
- Xinjiang Uygur Autonomous Region Disease Prevention and Control Center (CN)
- Heilongjiang Center for Tuberculosis Control and Prevention (CN)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-12-07
- DOI
- https://doi.org/10.1186/s12889-025-25844-w
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00