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Updated: Oct 26, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

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Published on: March 24, 2023

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

Chelzy Belitz¹, Hussnain Ali¹, John H L Hansen¹

¹CRSS: Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, TX, USA.

|July 26, 2021

Summary

This summary is machine-generated.

Machine learning can improve hearing aid (HA) retention by predicting optimal initial HA fittings from audiograms. This reduces adjustments, benefiting users and personal sound amplification products (PSAPs).

Keywords:

Hearing Aids audiogram audiometry classification clustering

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Last Updated: Oct 26, 2025

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Published on: January 23, 2017

Area of Science:

Audiology
Machine Learning
Biomedical Engineering

Background:

Hearing impairment affects millions, with low hearing aid (HA) adoption rates.
Suboptimal HA fittings and lengthy adjustment periods contribute to poor HA retention.

Purpose of the Study:

To develop a machine learning (ML) protocol for predicting optimal initial HA configurations.
To enhance HA retention by reducing the time to best operation.

Main Methods:

Utilized a large dataset of over 90,000 audiogram-fitting pairs.
Applied ML clustering methods (Birch, Ward, k-means) to identify preset HA configurations.
Employed classification methods to map audiograms to predicted configurations.

Main Results:

Identified a limited number of preset HA configurations based on clustering audiogram-fitting data.
Demonstrated the potential to reduce adjustment cycles between initial and final HA fittings.
Showcased ML's utility for initial HA fitting and PSAP configurations.

Conclusions:

ML clustering offers a promising approach to optimize initial hearing aid fittings.
Reducing adjustment time can significantly improve user satisfaction and HA retention.
This ML protocol can serve as a foundation for improved hearing healthcare delivery and accessible sound amplification devices.