It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
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.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Jiangmen Central Hospital, Department of Pulmonary and Critical Care Medicine, Jiangmen Institute of Respiratory Disease, Jiangmen, People’s Republic of China (GRID:grid.459671.8) (ISNI:0000 0004 1804 5346)
2 Guangdong Medical University, Dongguan Key Laboratory of Environmental Medicine, Department of Environmental and Occupational Health, School of Public Health, Dongguan, People’s Republic of China (GRID:grid.410560.6) (ISNI:0000 0004 1760 3078)
3 Southern Medical University, Dongguan People’s Hospital, Dongguan, People’s Republic of China (GRID:grid.284723.8) (ISNI:0000 0000 8877 7471)
4 University of California San Diago, Herbert Wertheim School of Public Health, Warrant College, San Diago, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242)