Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations
Abstract This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induced hearing loss (ONIHL)) at Hebei Medical...
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| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93976-5 |
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| author | Yong Li Xin Sun Yongtao Qu Shuling Yang Yueyi Zhai Yan Qu |
| author_facet | Yong Li Xin Sun Yongtao Qu Shuling Yang Yueyi Zhai Yan Qu |
| author_sort | Yong Li |
| collection | DOAJ |
| description | Abstract This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induced hearing loss (ONIHL)) at Hebei Medical Examination Center (2017–2023) were analyzed. Workers with combined exposure and without specific contraindications or other hearing impairment causes were included. Demographic and clinical data were gathered. Chi-square and Mann-Whitney U tests examined variables, and multivariate logistic regression determined ONIHL risk factors. Machine learning algorithms like Logistic Regression and Random Forest were developed, optimized, and evaluated. Results showed significant differences in gender, exposure, blood pressure, smoking, etc. between ONIHL and non-ONIHL groups. Male gender, combined exposure, diastolic blood pressure elevation, smoking, fasting blood glucose elevation, and age were positive predictors, while systolic blood pressure elevation was negative. The logistic model had the highest predictive ability (ROC = 0.714). Subgroup analysis revealed a significant positive correlation in specific subgroups. In summary, combined exposure increased ONIHL risk and affected health. Machine learning effectively predicted ONIHL, but the study had limitations and needed further research. |
| format | Article |
| id | doaj-art-203c8d8a64184872b3ad83d846becbfe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-203c8d8a64184872b3ad83d846becbfe2025-08-20T03:41:49ZengNature PortfolioScientific Reports2045-23222025-03-0115111010.1038/s41598-025-93976-5Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populationsYong Li0Xin Sun1Yongtao Qu2Shuling Yang3Yueyi Zhai4Yan Qu5Department of Otolaryngology, Hebei Medical UniversityHebei North UniversityDepartment of Otolaryngology, Hebei General HospitalAnimal Laboratory, The Third Hospital of Hebei Medical UniversityAnimal Laboratory, The Third Hospital of Hebei Medical UniversityDepartment of Otolaryngology, Hebei Medical UniversityAbstract This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk factors via machine learning techniques. Data from 14,145 workers (627 with occupational noise-induced hearing loss (ONIHL)) at Hebei Medical Examination Center (2017–2023) were analyzed. Workers with combined exposure and without specific contraindications or other hearing impairment causes were included. Demographic and clinical data were gathered. Chi-square and Mann-Whitney U tests examined variables, and multivariate logistic regression determined ONIHL risk factors. Machine learning algorithms like Logistic Regression and Random Forest were developed, optimized, and evaluated. Results showed significant differences in gender, exposure, blood pressure, smoking, etc. between ONIHL and non-ONIHL groups. Male gender, combined exposure, diastolic blood pressure elevation, smoking, fasting blood glucose elevation, and age were positive predictors, while systolic blood pressure elevation was negative. The logistic model had the highest predictive ability (ROC = 0.714). Subgroup analysis revealed a significant positive correlation in specific subgroups. In summary, combined exposure increased ONIHL risk and affected health. Machine learning effectively predicted ONIHL, but the study had limitations and needed further research.https://doi.org/10.1038/s41598-025-93976-5Occupational exposureNoise and dustHearing lossMachine learning algorithmsRisk factors |
| spellingShingle | Yong Li Xin Sun Yongtao Qu Shuling Yang Yueyi Zhai Yan Qu Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations Scientific Reports Occupational exposure Noise and dust Hearing loss Machine learning algorithms Risk factors |
| title | Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations |
| title_full | Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations |
| title_fullStr | Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations |
| title_full_unstemmed | Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations |
| title_short | Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations |
| title_sort | applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations |
| topic | Occupational exposure Noise and dust Hearing loss Machine learning algorithms Risk factors |
| url | https://doi.org/10.1038/s41598-025-93976-5 |
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