Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning
Machine learning algorithms can predict the risk of cognitive impairment in this population, with the XGBoost algorithm showing the best performance. The developed model can guide the implementation of appropriate preventive measures for dust-exposed workers.
Authors
- Fulin Cai (ORCID: https://orcid.org/0000-0002-1131-9610)
- Guangyao Si
- Yafeng Liu (ORCID: https://orcid.org/0000-0003-3930-4872)
- Sheng Xue (ORCID: https://orcid.org/0000-0001-5923-1119)
- Xiufeng Chen (ORCID: https://orcid.org/0000-0003-1798-3851)
- Jiale He (ORCID: https://orcid.org/0009-0002-6240-0256)
- Mei Zhang (ORCID: https://orcid.org/0000-0003-2717-7167)
Institutions
- Anhui University of Science and Technology (CN)
- UNSW Sydney (AU)
Publication Details
- Journal
- Ecotoxicology and Environmental Safety
- Published
- 2024-09-26
- DOI
- https://doi.org/10.1016/j.ecoenv.2024.117111
- Citations
- 5
- Primary Topic
- Air Quality and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 1.57