Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach

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• 作者：Mohsen Aliabadi ; Maryam Farhadian…
• 关键词：Artificial intelligence ; Hearing loss ; Noise exposure ; Prediction model
• 刊名：International Archives of Occupational and Environmental Health
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：88
• 期：6
• 页码：779-787
• 全文大小：570 KB
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  O
• 作者单位：Mohsen Aliabadi (1)
  Maryam Farhadian (2)
  Ebrahim Darvishi (3)
  
  1. Department of Occupational Health, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran
  2. Department of Biostatistics, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran
  3. Department of Occupational Health, School of Public Health, Kurdistan University of Medical Science, Kurdistan, Iran
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Occupational and Industrial Medicine
  Environmental Medicine/Environmental Psychology
  Rehabilitation
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1246

文摘

Purpose Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using artificial neural networks, this study aims to present an empirical model for the prediction of the hearing loss threshold among noise-exposed workers. Methods Two hundred and ten workers employed in a steel factory were chosen, and their occupational exposure histories were collected. To determine the hearing loss threshold, the audiometric test was carried out using a calibrated audiometer. The personal noise exposure was also measured using a noise dosimeter in the workstations of workers. Finally, data obtained five variables, which can influence the hearing loss, were used for the development of the prediction model. Multilayer feed-forward neural networks with different structures were developed using MATLAB software. Neural network structures had one hidden layer with the number of neurons being approximately between 5 and 15 neurons. Results The best developed neural networks with one hidden layer and ten neurons could accurately predict the hearing loss threshold with RMSE?=?2.6?dB and R 2?=?0.89. The results also confirmed that neural networks could provide more accurate predictions than multiple regressions. Conclusions Since occupational hearing loss is frequently non-curable, results of accurate prediction can be used by occupational health experts to modify and improve noise exposure conditions.