Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study
The aim of the study study was to model, with the use of a neural network algorithm, the significance of a variety of factors influencing the development of hearing loss among industry workers. The workers were categorized into three groups, according to the A-weighted equivalent sound pressure leve...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2020-04-01
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| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2404 |
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| author | Sajad ZARE Mohammad Reza GHOTBIRAVANDI Hossein ELAHISHIRVAN Mostafa Ghazizadeh AHSAEED Mina ROSTAMI Reza ESMAEILI |
| author_facet | Sajad ZARE Mohammad Reza GHOTBIRAVANDI Hossein ELAHISHIRVAN Mostafa Ghazizadeh AHSAEED Mina ROSTAMI Reza ESMAEILI |
| author_sort | Sajad ZARE |
| collection | DOAJ |
| description | The aim of the study study was to model, with the use of a neural network algorithm, the significance of a variety of factors influencing the development of hearing loss among industry workers. The workers were categorized into three groups, according to the A-weighted equivalent sound pressure level of noise exposure: Group 1 (LAeq < 70 dB), Group 2 (LAeq 70–80 dB), and Group 3 (LAeq > 85 dB). The results obtained for Group 1 indicate that the hearing thresholds at the frequencies of 8 kHz and 1 kHz had the maximum effect on the development of hearing loss. In Group 2, the factors with maximum weight were the hearing threshold at 4 kHz and the worker’s age. In Group 3, maximum weight was found for the factors of hearing threshold at a frequency of 4 kHz and duration of work experience. The article also reports the results of hearing loss modeling on combined data from the three groups. The study shows that neural data mining classification algorithms can be an effective tool for the identification of hearing hazards and greatly help in designing and conducting hearing conservation programs in the industry. |
| format | Article |
| id | doaj-art-130446bdcf75475a99a794bedc32aafc |
| institution | DOAJ |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2020-04-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-130446bdcf75475a99a794bedc32aafc2025-08-20T03:16:24ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2020-04-0145210.24425/aoa.2020.133150Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field StudySajad ZARE0Mohammad Reza GHOTBIRAVANDI1Hossein ELAHISHIRVAN2Mostafa Ghazizadeh AHSAEED3Mina ROSTAMI4Reza ESMAEILI5Kerman University of Medical Sciences and Health ServicesKerman University of Medical Sciences and Health ServicesKerman University of Medical SciencesShahid Bahonar University of KermanShahid Bahonar University of KermanHamedan University of Medical SciencesThe aim of the study study was to model, with the use of a neural network algorithm, the significance of a variety of factors influencing the development of hearing loss among industry workers. The workers were categorized into three groups, according to the A-weighted equivalent sound pressure level of noise exposure: Group 1 (LAeq < 70 dB), Group 2 (LAeq 70–80 dB), and Group 3 (LAeq > 85 dB). The results obtained for Group 1 indicate that the hearing thresholds at the frequencies of 8 kHz and 1 kHz had the maximum effect on the development of hearing loss. In Group 2, the factors with maximum weight were the hearing threshold at 4 kHz and the worker’s age. In Group 3, maximum weight was found for the factors of hearing threshold at a frequency of 4 kHz and duration of work experience. The article also reports the results of hearing loss modeling on combined data from the three groups. The study shows that neural data mining classification algorithms can be an effective tool for the identification of hearing hazards and greatly help in designing and conducting hearing conservation programs in the industry.https://acoustics.ippt.pan.pl/index.php/aa/article/view/2404noisemodelingnoise induced hearing loss (NIHL)neural network algorithm |
| spellingShingle | Sajad ZARE Mohammad Reza GHOTBIRAVANDI Hossein ELAHISHIRVAN Mostafa Ghazizadeh AHSAEED Mina ROSTAMI Reza ESMAEILI Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study Archives of Acoustics noise modeling noise induced hearing loss (NIHL) neural network algorithm |
| title | Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study |
| title_full | Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study |
| title_fullStr | Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study |
| title_full_unstemmed | Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study |
| title_short | Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study |
| title_sort | modeling and predicting the changes in hearing loss of workers with the use of a neural network data mining algorithm a field study |
| topic | noise modeling noise induced hearing loss (NIHL) neural network algorithm |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2404 |
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