Machine learning prediction of coal workers’ pneumoconiosis classification based on few-shot clinical data
The integration of clinical biochemical examination data with ML models, especially the RF-Adaboost and support vector machine-particle swarm optimization models, effectively predicted the staging of CWP. The proposed integrated feature selection method, which considered both the importance and independence of features, significantly enhanced model performance, providing a valuable tool for early screening and diagnosis of CWP.
Authors
- Songquan Wang (ORCID: https://orcid.org/0000-0001-5463-2263)
- Dekun Zhang (ORCID: https://orcid.org/0000-0002-2742-8598)
- Yuming Cui (ORCID: https://orcid.org/0000-0001-5648-1825)
- Jingying Huang
- Haiquan Li (ORCID: https://orcid.org/0000-0002-8049-0278)
- Wenlu Hang
- Jiaqi Jia
Institutions
- Jiangsu Normal University (CN)
- Xuzhou Medical College (CN)
- China University of Mining and Technology (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251359498
- Citations
- 2
- Primary Topic
- Occupational and environmental lung diseases
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province