Estimating hearing aid fitting presets with machine learning-based clustering strategies

Abstract

Although there exist nearly 35 × 106 hearing impaired people in the U.S., only an estimated 25% use hearing aids (HA), while others elect not to use prescribed HAs. Lack of HA acceptance can be attributed to several factors including (i) performance variability in diverse environments, (ii) time-to-convergence for best HA operating configuration, (iii) unrealistic expectations, and (iv) cost/insurance. This study examines a nationwide dataset of pure-tone audiograms and HA fitting configurations. An overview of data characteristics is presented, followed by use of machine learning clustering to suggest ways of obtaining effective starting configurations, thereby reducing time-to-convergence to improve HA retention.