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Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor

Published online by Cambridge University Press:  09 May 2024

Emre Soylemez Open the ORCID record for Emre Soylemez [Opens in a new window] ,
Isa Avci ,
Elif Yildirim ,
Engin Karaboya ,
Nihat Yilmaz ,
Süha Ertugrul  and
Suna Tokgoz-Yilmaz
Emre Soylemez*
Affiliation:
Department of Audiometry, Vocational School of Health Services, Karabuk University, Karabuk, Türkiye Audiology and Speech Pathology Ph.D. Program, Health Sciences Institute, Ankara University, Ankara, Türkiye
Isa Avci
Affiliation:
Department of Computer Engineering, Karabuk University, Karabuk, Türkiye
Elif Yildirim
Affiliation:
Department of Computer Engineering, Karabuk University, Karabuk, Türkiye
Engin Karaboya
Affiliation:
Department of Audiology, Karabuk Training and Research Hospital, Karabuk, Türkiye
Nihat Yilmaz
Affiliation:
Department of Otorhinolaryngology, Karabuk University, Karabuk, Türkiye
Süha Ertugrul
Affiliation:
Department of Otorhinolaryngology, Karabuk University, Karabuk, Türkiye
Suna Tokgoz-Yilmaz
Affiliation:
Department of Audiology, Faculty of Health Sciences, Ankara University, Ankara, Türkiye Audiology, Balance and Speech Disorders Unit, Medical Faculty, Ankara University, Ankara, Türkiye
*
Corresponding author: Emre Soylemez; Email: [email protected]
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Abstract

Objectives

This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models’ accuracy.

Methods

Two hundred workers employed in a metal industry were selected for this study and tested using pure tone audiometry. Their occupational exposure histories were collected, analysed and used to create a dataset. Eighty per cent of the data collected was used to train six machine learning models and the remaining 20 per cent was used to test the models.

Results

Eight workers (40.5 per cent) had bilaterally normal hearing and 119 (59.5 per cent) had hearing loss. Tinnitus was the second most important indicator after age for noise-induced hearing loss. The support vector machine was the best-performing algorithm, with 90 per cent accuracy, 91 per cent F1 score, 95 per cent precision and 88 per cent recall.

Conclusion

The use of tinnitus as a risk factor in the support vector machine model may increase the success of occupational health and safety programmes.

Keywords

machine learningnoise-induced hearing lossoccupational diseasesoccupational groups
Type
Main Article
Information
The Journal of Laryngology & Otology , Volume 138 , Issue 10 , October 2024 , pp. 1030 - 1035
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of J.L.O. (1984) LIMITED

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Footnotes

Emre Soylemez takes responsibility for the integrity of the content of the paper

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