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

  • Original Article
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International Archives of Occupational and Environmental Health Aims and scope Submit manuscript

Abstract

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.

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Acknowledgments

The authors would like to thank managers of steel factory for providing financial support for this project. Also, thanks to workers for their cooperation.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Occupational Health, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran

    Mohsen Aliabadi

  2. Department of Biostatistics, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran

    Maryam Farhadian

  3. Department of Occupational Health, School of Public Health, Kurdistan University of Medical Science, Kurdistan, Iran

    Ebrahim Darvishi

Authors
  1. Mohsen Aliabadi
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  2. Maryam Farhadian
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  3. Ebrahim Darvishi
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Correspondence to Mohsen Aliabadi.

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Aliabadi, M., Farhadian, M. & Darvishi, E. Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach. Int Arch Occup Environ Health 88, 779–787 (2015). https://doi.org/10.1007/s00420-014-1004-z

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  • DOI: https://doi.org/10.1007/s00420-014-1004-z

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