A Machine Learning Based Clustering Protocol for Determining Hearing Aid Initial Configurations from Pure-Tone Audiograms.

ABSTRACT: Of the nearly 35 million people in the USA who are hearing impaired, only an estimated 25% use hearing aids (HA). A good number of HAs are prescribed but not used partially because of the time to convergence for best operation between the audiologist and user. To improve HA retention, it is suggested that a machine learning (ML) protocol could be established which improves initial HA configurations given a user's pure-tone audiogram. This study examines a ML clustering method to predict the best initial HA fitting from a corpus of over 90,000 audiogram-fitting pairs collected from hearing centers throughout the USA. We first examine the final HA comfort targets to determine a limited number of preset configurations using several multi-dimensional clustering methods (Birch, Ward, and k-mean

SUBMITTER: Belitz C 

PROVIDER: S-EPMC8299699 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature