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This paper is in the following e-collection/theme issue:

Cross-Sectional Studies in Public Health (1326) Machine Learning (3318) Occupational Health and Ergonomics/Prevention at the Workplace (399) Hearing Impairment and Hearing Loss (103) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (175)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60373, first published .
Young girl with long brown hair covering her ears, smiling against a teal background.

Optimizing a Classification Model to Evaluate Individual Susceptibility in Noise-Induced Hearing Loss: Cross-Sectional Study

Optimizing a Classification Model to Evaluate Individual Susceptibility in Noise-Induced Hearing Loss: Cross-Sectional Study

Authors of this article:

Shiyuan Li1, 2, 3 Author Orcid Image ;   Xiao Yu1, 2, 3 Author Orcid Image ;   Xinrong Ma1, 2, 3 Author Orcid Image ;   Ying Wang1, 2, 3 Author Orcid Image ;   Junjie Guo1 Author Orcid Image ;   Jiping Wang1, 2, 3 Author Orcid Image ;   Wenxin Shen1, 2, 3 Author Orcid Image ;   Hongyu Dong1, 2, 3 Author Orcid Image ;   Richard Salvi4 Author Orcid Image ;   Hui Wang1, 2, 3 Author Orcid Image ;   Shankai Yin1, 2, 3 Author Orcid Image
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Journals

  1. Kaveh M, Zamanian Z, Firoozichahak A, Soleymani-Ghoozhdi D. Applications of artificial intelligence in occupational noise assessment and noise‑related health outcomes: a scoping review. International Journal of Environmental Health Research 2026:1 View