Machine Learning Models Can Predict Tinnitus and Noise-Induced Hearing Loss.

ABSTRACT:

Objectives

Despite the extensive use of machine learning (ML) models in health sciences for outcome prediction and condition classification, their application in differentiating various types of auditory disorders remains limited. This study aimed to address this gap by evaluating the efficacy of five ML models in distinguishing (a) individuals with tinnitus from those without tinnitus and (b) noise-induced hearing loss (NIHL) from age-related hearing loss (ARHL).

Design

We used data from a cross-sectional study of the Canadian population, which included audiologic and demographic information from 928 adults aged 30 to 100 years, diagnosed with either ARHL or NIHL due to long-term occupational noise exposure. The ML models applied in this study were artificial neural network

SUBMITTER: Jafari Z 

PROVIDER: S-EPMC12352570 | biostudies-literature | 2025 Sep-Oct 01

REPOSITORIES: biostudies-literature