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

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dc.contributor.authorSoylemez, E.
dc.contributor.authorAvci, I.
dc.contributor.authorYildirim, E.
dc.contributor.authorKaraboya, E.
dc.contributor.authorYilmaz, N.
dc.contributor.authorErtugrul, S.
dc.contributor.authorTokgoz-Yilmaz, S.
dc.date.accessioned2024-09-29T16:21:06Z
dc.date.available2024-09-29T16:21:06Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractObjective: This study aims to determine which machine learning (ML) model is most suitable for predicting noise-induced hearing loss (NIHL) and the effect of tinnitus on the models' accuracy. Method: 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 percent of the data collected was used to train six ML models, and the remaining 20% was used to test the models. Results: Eight (40.5%) workers had bilaterally normal hearing, and 119 (59.5%) had hearing loss. Tinnitus was the second most important indicator after age for NIHL. The support vector machine (SVM) was the best-performing algorithm with 90% accuracy, 91% F1-score, 95% precision, and 88% recall. Conclusion: The use of tinnitus as a risk factor in the SVM model may increase the success of occupational health and safety programs. © 2024 Cambridge University Press. All rights reserved.en_US
dc.identifier.doi10.1017/S002221512400094X
dc.identifier.issn0022-2151
dc.identifier.pmid38719484en_US
dc.identifier.scopus2-s2.0-85193069022en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1017/S002221512400094X
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9549
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherCambridge University Pressen_US
dc.relation.ispartofJournal of Laryngology and Otologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectnoise-induced hearing lossen_US
dc.subjectoccupational diseaseen_US
dc.subjectworkersen_US
dc.titlePredicting noise-induced hearing loss with machine learning: The influence of tinnitus as a predictive factoren_US
dc.typeArticleen_US

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