Investigation of factors influencing abnormal pulmonary ventilation function in occupational exposed populations and the establishment of a risk prediction model.
Dust
Machine learning
Occupational health
Pulmonary ventilation function
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
24 Oct 2024
24 Oct 2024
Historique:
received:
11
04
2024
accepted:
14
10
2024
medline:
25
10
2024
pubmed:
25
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
The purpose of this study is to investigate the influencing factors of abnormal pulmonary ventilation function in occupational exposed populations and to establish a risk prediction model. The findings will provide a basis for formulating corresponding strategies for the prevention and treatment of occupational diseases. The study focused on workers who underwent occupational health examinations in the year 2020. Statistical analysis was conducted using methods such as t-tests, chi-square tests, and multiple logistic regression analysis. Additionally, machine learning methods were employed to establish multiple models to address classification problems. Among the 7472 workers who participated in the occupational health examination, 1681 cases of abnormal pulmonary ventilation function were detected, resulting in a detection rate of 22.6%. Based on the analysis of occupational hazard data, a risk prediction model was established. Age, work tenure, type of the employing enterprise, and type of dust exposure are all identified as driving factors for abnormal pulmonary function. These factors were used as predictive variables for establishing the risk prediction model. Among the various models evaluated, the logistic regression model was found to be the optimal model for predicting abnormal pulmonary ventilation function.
Identifiants
pubmed: 39448756
doi: 10.1038/s41598-024-76412-y
pii: 10.1038/s41598-024-76412-y
doi:
Substances chimiques
Dust
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25215Informations de copyright
© 2024. The Author(s).
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