Predicting and classifying hearing loss in sailors working on speed vessels using neural networks: a field study
Main Article Content
Keywords
Noise, Noise-induced Hearing Loss (NIHL), Modelling, Neural Network (NN)
Abstract
Background: Noise-induced hearing loss (NIHL) is one of the main risk factors affecting people's health and wellbeing in the workplace. Analysing NIHL and consequently controlling the causing factors can significantly affect the improvement of working environments. Methods: One hundred and twelve male sailors participated in this study. They were classified into three groups depending on occupational noise exposure: (A) none, i.e., sound pressure level (SPL) lower than 70dBA, (B) exposed to SPL in the range of 70-85dBA, and (C) exposed to SPL exceeding 80dBA. In a first phase, hearing loss shaping risk factors were identified and analysed, including hearing loss in different frequencies, age, work experience, sound pressure level (SPL), marital status, and systolic and diastolic blood pressure. Then, neural networks were trained to predict the hearing loss changes of personnel and used to determine the weight of hearing loss factors. Finally, the accuracy of predicting models was calculated relying on Bayesian statistics. Results and conclusion: In the present study using neural networks, five models were developed. Their accuracy ranged from 92% to 100%. The frequencies of 4000Hz and 2000Hz showed the strongest association with the hearing loss of the sailors. Also, including systolic and diastolic blood pressure did not have any impact on predicted hearing loss, indicating that SPL was poorly correlated with extra-auditory effects.
References
2. Nassiri P, Zare S, Monazzam MR, et al. Evaluation of the effects of various sound pressure levels on the level of serum aldosterone concentration in rats. Noise & Health. 2017;19(89),200. https://doi.org/10.4103/nah.NAH_64_16
3. Mohd Nawi N, Rehman MZ, Ghazali MI, et al. Hybrid Bat-BP: A New Intelligent Tool for Diagnosing Noise-Induced Hear-ing Loss (NIHL) in Malaysian Industrial Workers. Appl Mech Mater. 2014;652-656. https://doi.org/10.4028/www.scientific.net/AMM.465-466.652
4. Al-Dayyeni WS, Sun P, Qin J. Investigations of Auditory Filters Based Excitation Patterns for Assessment of Noise In-duced Hearing Loss. Arch Acoust. 2018;43,477-486.
5. Muzaffar S, Orr L, Rickard R, et al. Mitigating noise-induced hearing loss after blast injury. Trauma. 2019;21(2),121-127. https://doi.org/10.1177/1460408618755191
6. Moore BC. A review of the perceptual effects of hearing loss for frequencies above 3 kHz. Int J Audiol. 2016;55(12),707-714. https://doi.org/10.1080/14992027.2016.1204565
7. Le TN, Straatman LV, Lea J, Westerberg B. Current insights in noise-induced hearing loss: a literature review of the un-derlying mechanism, pathophysiology, asymmetry, and management options. J Otolaryngol-Head N. 2017;46(1),41.
https://doi.org/10.1186/s40463-017-0219-x
8. Samant Y, Parker D, Wergeland E, Wannag A. The Norwegian labour inspectorate's registry for work-related diseases: Data from 2006. Int J Occup Environ Health. 2008;14(4),272-279. https://doi.org/10.1179/oeh.2008.14.4.272
9. Lie A, Skogstad M, Johannessen HA, et al. Occupational noise exposure and hearing: a systematic review. Int Arch Occup Environ Health. 2016;89(3),351-372. https://doi.org/10.1007/s00420-015-1083-5
10. Leong MS. Noise and Vibration Problems: How they effect us and the industry in the Malaysian Context. University Teknologi Malaysia, Skudai, Johar, Malaysia. 2003; 01-13.
11. Golmohammadi R, Ziad M, Atari SG. Assessment of Noise Pollution and Its Effects on Stone Cut Industry Workers of Malayer District. Iran Occup Health. 2006;3,23–27.
12. Österman C, Hult C, Praetorius G. Occupational safety and health for service crew on passenger ships. Saf Sci. 2020;121,403-413. https://doi.org/10.1016/j.ssci.2019.09.024
13. Turan O, Helvacioglu I, Insel M, et al. Crew noise exposure on board ships and comparative study of applicable stand-ards. Ships Offshore Struct. 2011;6(4),323-338. https://doi.org/10.1080/17445302.2010.514716
14. Krystosik-Gromadzińska A. Ergonomic assessment of selected workstations on a merchant ship. Int J Occup Saf Ergon. 2018;24(1),91-99. https://doi.org/10.1080/10803548.2016.1273589
15. International Maritime Organization (IMO). Adoption of the Code on Noise Levels on Board Ships. International Mari-time Organization, London, United Kingdom. 2012;337.
16. Albizu EJ, de Oliveira Gonçalves CG, de Lacerda ABM, Marques JM. Noise exposure and effects on hearing in Brazilian fishermen. Work. 2020;1-9. https://doi.org/10.3233/WOR-203139
17. Zytoon MA. Occupational noise exposure of fishermen aboard small and medium-scale fishing vessels. Int J Ind Ergon. 2013;43(6),487-494. https://doi.org/10.1016/j.ergon.2012.08.001
18. DimitrovÐ T, Vodenicharov V. Hearing loss among ship crewmembers. Varna Medical Forum. 2017;5,35-39.
19. Roiger RJ. Data mining: a tutorial-based primer. Chapman and Hall: London, United Kingdom; 2017. https://doi.org/10.1201/9781315382586
20. Zare S, Ghotbi-Ravandi MR, ElahiShirvan H, et al. Predicting and weighting the factors affecting workers' hearing loss based on audiometric data using C5 algorithm. Ann Glob Health. 2019;85(1). https://doi.org/10.5334/aogh.2522
21. Rehman M, Nawi NM, Ghazali MI. Predicting noise-induced hearing loss (NIHL) and hearing deterioration index (HDI) in Malaysian industrial workers using GDAM algorithm. J Eng Technol. 2012;3,179-197.
22. Bing D, Ying J, Miao J, et al. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol. 2018;43(3),868-874. https://doi.org/10.1111/coa.13068
23. Saduf MAW, Wani A. Comparative study of back propagation learning algorithms for neural networks. Int j adv res comput sci eng inf technol. 2013;3(12).
24. Shahiri AM, Husain W. A review on predicting student's performance using data mining techniques. Procedia Comput Sci. 2015;72,414-422. https://doi.org/10.1016/j.procs.2015.12.157
25. Zare S, Ghotbi-Ravandi MR, ElahiShirvan H, et al. Modelling and Predicting the Changes in Hearing Loss of Workers with the Use of a Neural Network Data Mining Algorithm: A Field Study. Arch Acoust. 2020;45(2),303–311.
https://doi.org/10.24425/aoa.2020.133150
26. ISO 1999. Acoustics—estimation of noise-induced hearing loss, in international standard. Switzerland: ISO. 2013;24.
27. WHO. Report of the informal working group on prevention of deafness and hearing impairment programme planning. Geneva: WHO. 1991,18-21 21 June.
28. Manninen O, Aro S. Noise-induced hearing loss and blood pressure. Int Arch Occup Environ Health. 1979;42(3):251-256. https://doi.org/10.1007/BF00377779
29. ISO 9612: 2009 Acoustics--Determination of occupational noise exposure--Engineering method. 2009.
30. Golmohammadi R, Aliabadi M. Noise and vibration engineering. student publication: Hamadan, Iran. 2010;557-278.
31. Craven MW, Shavlik JW. Using neural networks for data mining. Future Gener Comput Syst. 1997;13(2-3),211-229.
https://doi.org/10.1016/S0167-739X(97)00022-8
32. Sumathi S, Sivanandam S. Introduction to data mining and its applications. Springer: Berlin, Germany. 2006.
https://doi.org/10.1007/978-3-540-34351-6
33. Singh Y, Chauhan AS. Neural Networks In Data Mining. J Theor Appl Inf Technol. 2009;5(1).
34. Larose DT, Larose CD. Discovering knowledge in data: an introduction to data mining. John Wiley & Sons: London, United Kingdom. 2014. https://doi.org/10.1002/9781118874059
35. Levin JL, Curry III WF, Shepherd S, et al. Hearing loss and noise exposure among commercial fishermen in the gulf coast. J Occup Environ Med. 2016;58(3),306-313. https://doi.org/10.1097/JOM.0000000000000642
36. Paini MC, Morata TC, Corteletti LJ, et al. Audiological findings among workers from Brazilian small-scale fisheries. Ear Hear. 2009;30(1),8-15. https://doi.org/10.1097/AUD.0b013e31818fba17
37. Nguyen NPT, Le DD, Nguyen MDM, et al. Noise exposure and its relationship with hypertension among fishermen in thua thien hue province, vietnam. J Integr Community Health. 2020;9(1),3-16. https://doi.org/10.24321/2319.9113.202001
38. Jegaden D. Noise aboard ships: its effects on the hearing of merchant seamen. Arch Mal Prof. 1984;45(5):345-349.
39. Tu M, Jepsen JR. Hypertension among Danish seafarers. Int Marit Health. 2016;67(4),196-204. https://doi.org/10.5603/IMH.2016.0037
40. Jégaden D, Le Pluart C, Marie Y, Piquemal B. Contribution to the study noise-high blood pressure. Concerning 455 mer-chant sailors aged 40–55 years. Arch Mal Prof. 1986;47,15-20.
41. Farhadian M, Aliabadi M, Darvishi E. Empirical estimation of the grades of hearing impairment among industrial work-ers based on new artificial neural networks and classical regression methods. Indian J Occup Environ. 2015;19(2),84. https://doi.org/10.4103/0019-5278.165337
42. Aliabadi M, Farhadian M, Darvishi E. Prediction of hearing loss among the noise-exposed workers in a steel factory us-ing artificial intelligence approach. Int Arch Occup Environ Health. 2015;88(6),779-787. https://doi.org/10.1007/s00420-014-1004-z
Article Sidebar
Article Details
How to Cite
All Journal's articles are Open Access papers distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reproductions with commercial intent will require written permission and payment of royalties.
This work is licensed under a CC BY-NC 4.0 Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
