Machine Learning Models Can Predict Tinnitus and Noise-Induced Hearing Loss
Our findings highlight the application of ML models, particularly ANN and RF, in advancing diagnostic precision for tinnitus and NIHL, potentially providing a framework for integrating ML techniques into clinical audiology for improved diagnostic precision. Future research is suggested to expand datasets to include diverse populations and integrate longitudinal data.
Authors
- Zahra Jafari (ORCID: https://orcid.org/0000-0002-0485-2003)
- Majid H. Mohajerani (ORCID: https://orcid.org/0000-0003-0964-2977)
- Bryan E Kolb (ORCID: https://orcid.org/0000-0002-2576-0642)
- Glenn Hole
- Ryan Harari
Institutions
- University of Lethbridge (CA)
- Dalhousie University (CA)
- Harvard University (US)
- Harvard University Press (US)
- Mass General Brigham (US)
- McGill University (CA)
Publication Details
- Journal
- Ear and Hearing
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1097/aud.0000000000001670
- Citations
- 8
- Primary Topic
- Hearing, Cochlea, Tinnitus, Genetics
- Type
- article
- Field-Weighted Citation Impact
- 11.72