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Data-driven audiogram classifier using data normalization and multi-stage feature selection.

Abeer Elkhouly^1,2,3, Allan Melvin Andrew^4,5, Hasliza A Rahim^6,7

¹Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia.

Scientific Reports

|February 1, 2023

Summary

This summary is machine-generated.

This study introduces a novel machine learning (ML) approach for classifying hearing aid audiogram shapes. The ML model significantly improves accuracy and efficiency in hearing aid fitting.

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Area of Science:

Audiology and Hearing Science
Machine Learning Applications
Signal Processing

Background:

Audiograms map hearing ability across frequencies, guiding hearing aid filter bank design.
Current hearing aid fitting faces challenges including audiologist shortages, complex designs, and lengthy processes.
Accurate audiogram classification is crucial for effective hearing aid customization.

Purpose of the Study:

To develop and evaluate a machine learning (ML) solution for classifying audiogram shapes.
To address limitations in current audiogram classification and hearing aid fitting procedures.
To introduce a novel ML technique for improved audiogram categorization.

Main Methods:

Utilized unsupervised spectral clustering for audiogram shape classification.
Developed unique features for the ML model to enhance audiogram description.
Applied and statistically analyzed various normalization methods to optimize the training dataset.
Implemented multi-stage feature selection for precise audiogram characterization.

Main Results:

The proposed ML algorithm demonstrated superior performance compared to existing models.
Achieved higher accuracy, precision, recall, specificity, and F-score values.
The multi-stage feature selection was identified as key to the improved performance.
The ML model effectively classifies audiograms based on their unique shapes.

Conclusions:

The developed ML technique offers a promising solution for audiogram classification.
This novel approach can enhance the efficiency and accuracy of hearing aid fitting.
Integration of this ML model can potentially transform current practices in audiogram analysis and hearing aid customization.