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Using Machine Learning and the National Health and Nutrition Examination Survey to Classify Individuals With Hearing

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Machine learning effectively estimated hearing loss using remote audiology methods, improving access to care. While accuracy was moderate, the approach ensured sufficient audibility for most individuals, showing promise for future remote hearing healthcare solutions.

Keywords:
CDCNHANESaudiologycenters for disease control and preventionmachine learningnational health and nutrition examination surveyremote audiology

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Area of Science:

  • Audiology and Hearing Healthcare
  • Machine Learning in Health
  • Biomedical Data Analysis

Background:

  • Growing demand for remote audiology solutions to enhance hearing healthcare access.
  • Prior work, like the Van Tasell method, aimed for at-home audiogram measurement without precise stimulus levels.
  • Need for validated, efficient methods for remote hearing assessment.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning algorithms for estimating audiograms using remote assessment principles.
  • To assess the feasibility of a simplified, home-based audiology testing approach.
  • To determine if estimated audiograms can guide hearing aid amplification effectively.

Main Methods:

  • Utilized the National Health and Nutrition Examination Survey (NHANES) database (9,256 cases) for training and testing.
  • Employed machine learning, specifically a random forest algorithm, to predict hearing loss categories (Wisconsin Age-Related Hearing Impairment Classification Scale - WARHICS).
  • Key predictors included: 2-4 kHz pure-tone slope, gender, age, military experience, and self-reported hearing ability.

Main Results:

  • The random forest model achieved 54.79% correct classification of hearing loss.
  • 34.40% were predicted to have milder loss, and 10.82% more severe loss than measured.
  • Despite classification inaccuracies, audibility calculations showed that under-amplification provided sufficient gain for ~95% correct speech intelligibility for 88% of individuals.

Conclusions:

  • Machine learning-based audiogram estimation shows promise for remote audiology, despite current classification limitations.
  • The method appears safe for hearing aid fitting, with minimal risk of over-amplification.
  • Further refinement is necessary for clinical application in remote hearing healthcare settings.