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Predicting noise-induced hearing loss with machine learning: The influence of tinnitus as a predictive factor
(Cambridge University Press, 2024) Soylemez, E.; Avci, I.; Yildirim, E.; Karaboya, E.; Yilmaz, N.; Ertugrul, S.; Tokgoz-Yilmaz, S.
Objective: 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.