{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T22:47:01Z","timestamp":1791672421101,"version":"4.3.4"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"10","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Laryngol. Otol."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S002221512400094X_sec_a1\"><jats:title>Objectives<\/jats:title><jats:p>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\u2019 accuracy.<\/jats:p><\/jats:sec><jats:sec id=\"S002221512400094X_sec_a2\" sec-type=\"methods\"><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S002221512400094X_sec_a3\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S002221512400094X_sec_a4\" sec-type=\"conclusions\"><jats:title>Conclusion<\/jats:title><jats:p>The use of tinnitus as a risk factor in the support vector machine model may increase the success of occupational health and safety programmes.<\/jats:p><\/jats:sec>","DOI":"10.1017\/s002221512400094x","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T01:12:55Z","timestamp":1715217175000},"page":"1030-1035","source":"Crossref","is-referenced-by-count":11,"title":["Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor"],"prefix":"10.1017","volume":"138","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7554-3048","authenticated-orcid":false,"given":"Emre","family":"Soylemez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isa","family":"Avci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elif","family":"Yildirim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Engin","family":"Karaboya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nihat","family":"Yilmaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00fcha","family":"Ertugrul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suna","family":"Tokgoz-Yilmaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2024,5,9]]},"container-title":["The Journal of Laryngology &amp; Otology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S002221512400094X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T09:57:40Z","timestamp":1732269460000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S002221512400094X\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":0,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["S002221512400094X"],"URL":"https:\/\/doi.org\/10.1017\/s002221512400094x","relation":{},"ISSN":["0022-2151","1748-5460"],"issn-type":[{"value":"0022-2151","type":"print"},{"value":"1748-5460","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,9]]}}}