Archives of Acoustics, 45, 2, pp. 303–311, 2020

Modeling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study

Sajad ZARE
Kerman University of Medical Sciences and Health Services
Iran, Islamic Republic of

Kerman University of Medical Sciences and Health Services
Iran, Islamic Republic of

Kerman University of Medical Sciences
Iran, Islamic Republic of

Mostafa Ghazizadeh AHSAEED
Shahid Bahonar University of Kerman
Iran, Islamic Republic of

Shahid Bahonar University of Kerman
Iran, Islamic Republic of

Hamedan University of Medical Sciences
Iran, Islamic Republic of

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.
Keywords: noise; modeling; noise induced hearing loss (NIHL); neural network algorithm
Full Text: PDF
Copyright © The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).


Ahmed H.O. et al. (2001), Occupational noise exposure and hearing loss of workers in two plants in eastern

Saudi Arabia, Annals of Occupational Hygiene, 45(5): 371–380, doi: 10.1093/annhyg/45.5.371.

Badr A., Mohammad Esmaeil S., Heidari H. (2009), Applying the data-mining technique in order to categorize the target users of the central library of isfahan university of technology (studying the motives and information seeking behaviors of them), Iranian Journal of Information Processing and Management, 33(1): 275–298.

Golabi M.R., Akhondali A.M., Radmanesh F. (2013), Comparison of the performace of different neural networks algorithm functions in simulation of seasonal precipitation case study: selected stations of Khuzestan province [in Persian], Journal of Geographical Sciences, 13(30): 151–169.

Golmohammadi R., Aliabadi M. (1999), Noise and vibration engineering, Daneshju, Hamedan.

Golmohammadi R., Zaman Parvar A.R., Khalili A. (2001), Assessment of the relationship between noise and hearing loss in workers Isfahan Steel Rolling Workshop, Scientific Journal of Hamadan University of Medical Sciences, 8(1): 35–38.

Golmohammadi R., Ziad M., Atari S. (2006), Assessment of noise pollution and its effects on stone cut industry workers of Malayer District, Iran Occupational Health Journal, 3(1–2): 23–27.

Gubbels S.P., Gartrell B.C., Ploch J.L., Hanson K.D. (2017), Can routine office-based audiometry predict cochlear implant evaluation results?, Laryngoscope, 127(1): 216–222, doi: 10.1002/lary.26066.

ISO 1999 (2013), Acoustics – Estimation of noiseinduced hearing loss.

ISO 9612 (2009), Acoustics – Determination of occupational noise exposure – Engineering method.

Kohzadi N., Boyd M.S., Kaastra I., Kermanshahi B.S., Scuse D. (1995), Neural networks for forecasting: an introduction, Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 43(3): 463–474, doi: 10.1111/j.1744-7976.1995.tb00135.x.

Krogh A., Vedelsby J. (1995), Neural network ensembles, cross validation, and active learning, Proceedings of the 7th International Conference on Neural Information Processing Systems, pp. 231–238, MIT Press, Cambridge, MA.

Larose D.T., Larose C.D. (2014), Discovering knowledge in data: an introduction to data mining, John Wiley & Sons.

Leong M.S. (2003), Noise and vibration problems how they affects us and the industry in the Malaysian context, Penerbit Universiti Teknologi Malaysia.

Majumder J., Sharma L.K. (2014), Application of data mining techniques to audiometric data among professionals in India, Journal of Scientific Research and Reports, 3(23): 2960–2971, doi: 10.9734/JSRR/2014/12700.

Masumi A., Rekabi H., Bayat A., Abshirini H. (2008), Comparison of noise induced hearing loss and impact noise, Med Sci, 7.

Mccullagh J. (2010), Data mining in sport: A neural network approach, International Journal of Sports Science and Engineering, 4(3): 131–138.

Nassiri P., Zare S., Monazzam Mr., Pourbakht A., Azam K., Golmohammadi T. (2016), Modeling signal- to-noise ratio of otoacoustic emissions in workers exposed to different industrial noise levels, Noise and Health, 18(85): 391–398, doi: 10.4103/1463-1741.195808.

Nawi N.M., Rehman M.Z., Ghazali M.I. (2011), Noise-induced hearing loss prediction in Malaysian industrial workers using gradient descent with adaptive momentum algorithm, International Review on Computers and Software, 6(5): 740–749.

Noma N., Khanapi M., Ghani A., Mohamad Khir A., Noorizan Y. (2013), Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier, Australian Journal of Basic and Applied Sciences, 7(9): 35–43.

Ramos-Miguel A., Perez-Zaballos T., Perez D., Falconb J.C., Ramosb A. (2015), Use of data mining to predict significant factors and benefits of bilateral cochlear implantation, European Archives of Oto- Rhino-Laryngology, 272(11): 3157–3162, doi: 10.1007/s00405-014-3337-3.

Safari Variani A., Ahmadi S., Zaroshani V., Ghorbanideh M. (2018), Water pump noise control using designed acoustic curtains in a residential building of Qazvin city, Iran Occupational Health Journal, 15(1): 126–135.

Schlauch R.S., Nelson P. (2009), Puretone evaluation, [In:] Handbook of Clinical Audiology, 6th ed., pp. 30–49, Wolters Kluwer/Lippincott Williams & Wilkins, Philadelphia.

Tajic R., Ghadami A., Ghamari F. (2008), The effects of noise pollution and hearing of metal workers in Arak, Zahedan Journal of Research in Medical Sciences, 10(4): e94504.

WHO (1991), Report of the Informal Working Group on Prevention of Deafness and Hearing Impairment Programme Planning, Geneva, June 18–21.

Zamanian Z., Mohammadi H., Rezaeeyani M.T., Dehghany M. (2012), An investigation of shift work disorders in security personnel of 3 hospitals of Shiraz University of Medical Sciences, 2009, Iran Occupational Health, 9(1): 52–57.

Zamanian Z., Dehghani M., Hashemi H. (2013), Outline of changes in cortisol and melatonin circadian rhythms in the security guards of shiraz university of medical sciences, International Journal of Preventive Medicine, 4(7): 825–830.

Zare S., Ghotbi-Ravandi M.R., Elahishirvan H., Ahsaee M.G., Rostami M. (2019), Predicting and weighting the factors affecting workers’ hearing loss based on audiometric data using C5 algorithm, Annals of Global Health, 85(1): 88, doi: 10.5334/aogh.2522.

DOI: 10.24425/aoa.2020.133150