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

Soylemez E., Avci I., Yildirim E., Karaboya E., Yilmaz N., Ertugrul S., ...Daha Fazla

Journal of Laryngology and Otology, cilt.138, sa.10, ss.1030-1035, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 138 Sayı: 10
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1017/s002221512400094x
  • Dergi Adı: Journal of Laryngology and Otology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, MLA - Modern Language Association Database, Veterinary Science Database
  • Sayfa Sayıları: ss.1030-1035
  • Anahtar Kelimeler: machine learning, noise-induced hearing loss, occupational diseases, occupational groups
  • Ankara Üniversitesi Adresli: Evet

Özet

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.