2024/05/09 by Emre Söylemez, Emre Soylemez, İsa Avcı +10
Health Professions · Neuroscience · #Hearing Loss and Rehabilitation #Hearing, Cochlea, Tinnitus, Genetics #Noise Effects and Management
paper · doi:10.1017/s002221512400094x
crossref issued 2024/05/09 · crossref published 2024/05/09 · crossref published-online 2024/05/09 · openalex publication_date 2024/05/09 · crossref created 2024/05/09 · crossref published-print 2024/10/01 · crossref deposited 2024/11/22 · openalex created_date 2025/10/10 · crossref indexed 2026/07/31 · openalex updated_date 2026/08/01
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.