Development of a machine learning-based risk prediction model for early-stage pneumoconiosis: a retrospective study
The indicators ultimately established as being associated with pneumoconiosis progression were WBC, PDW, TB, ANC, ALT and AST. The ML algorithm combined blood biochemical indicators to determine the risk factors associated with the occurrence of pneumoconiosis. The SVM model performs well and has the potential to improve early detection and diagnosis in clinical practice.
Authors
- Xinghua Li (ORCID: https://orcid.org/0000-0002-2971-159X)
- Zhaobo Guan
- Lin Zhao (ORCID: https://orcid.org/0000-0002-2719-3135)
- Yaru Jiang
- Wanping Wang
- Zhenyu Li
- Shaojie Li
- Hao Xu
- Xin Jin
Institutions
- Shanxi Medical University (CN)
- Shanxi University (CN)
- Changzhi Medical College (CN)
- Shanxi University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-07
- DOI
- https://doi.org/10.3389/fmed.2025.1730472
- Primary Topic
- Occupational and environmental lung diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00