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Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data

The initial software fitting prescribed by the fitting formula largely depends on the patient's hearing loss, which may not be the optimal preference for a particular user. Certain criteria must also be readjusted by an audiologist to meet the user-specific requirements. Therefore, this study f...

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Autores principales: Mondol, S. I. M. M. Raton, Kim, Hyun Ji, Kim, Kyu Sung, Lee, Sangmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588352/
https://www.ncbi.nlm.nih.gov/pubmed/36285186
http://dx.doi.org/10.1155/2022/1667672
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author Mondol, S. I. M. M. Raton
Kim, Hyun Ji
Kim, Kyu Sung
Lee, Sangmin
author_facet Mondol, S. I. M. M. Raton
Kim, Hyun Ji
Kim, Kyu Sung
Lee, Sangmin
author_sort Mondol, S. I. M. M. Raton
collection PubMed
description The initial software fitting prescribed by the fitting formula largely depends on the patient's hearing loss, which may not be the optimal preference for a particular user. Certain criteria must also be readjusted by an audiologist to meet the user-specific requirements. Therefore, this study focuses on the novel application of a neural network (NN) technique to build a suitable fitting algorithm with prescribed hearing loss and the corresponding preferred gain to minimize the gap between optimized fittings. The algorithm intended to learn the hearing preferences of an individual user such that the initial fitting may be optimized. These findings demonstrate the efficiency of the algorithm, with and without additional features. Using the clinical fitting data, the average mean square error (MSE) for the simple NN algorithm was 5.4183%. By adding additional features to the data, the algorithm performed better, and the average MSE was as low as 5.2530%. However, the algorithm outperformed Company A fitting software, as the MSE was the highest at 5.4748%. As the company's automatic fitting has a noticeable discrepancy with clinical fitting records, the impeccable results from this study can lead to a better path towards fitting satisfaction, thus benefiting the hearing-impaired community to a larger extent.
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spelling pubmed-95883522022-10-24 Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data Mondol, S. I. M. M. Raton Kim, Hyun Ji Kim, Kyu Sung Lee, Sangmin J Healthc Eng Research Article The initial software fitting prescribed by the fitting formula largely depends on the patient's hearing loss, which may not be the optimal preference for a particular user. Certain criteria must also be readjusted by an audiologist to meet the user-specific requirements. Therefore, this study focuses on the novel application of a neural network (NN) technique to build a suitable fitting algorithm with prescribed hearing loss and the corresponding preferred gain to minimize the gap between optimized fittings. The algorithm intended to learn the hearing preferences of an individual user such that the initial fitting may be optimized. These findings demonstrate the efficiency of the algorithm, with and without additional features. Using the clinical fitting data, the average mean square error (MSE) for the simple NN algorithm was 5.4183%. By adding additional features to the data, the algorithm performed better, and the average MSE was as low as 5.2530%. However, the algorithm outperformed Company A fitting software, as the MSE was the highest at 5.4748%. As the company's automatic fitting has a noticeable discrepancy with clinical fitting records, the impeccable results from this study can lead to a better path towards fitting satisfaction, thus benefiting the hearing-impaired community to a larger extent. Hindawi 2022-10-15 /pmc/articles/PMC9588352/ /pubmed/36285186 http://dx.doi.org/10.1155/2022/1667672 Text en Copyright © 2022 S. I. M. M. Raton Mondol et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mondol, S. I. M. M. Raton
Kim, Hyun Ji
Kim, Kyu Sung
Lee, Sangmin
Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data
title Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data
title_full Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data
title_fullStr Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data
title_full_unstemmed Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data
title_short Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data
title_sort machine learning-based hearing aid fitting personalization using clinical fitting data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588352/
https://www.ncbi.nlm.nih.gov/pubmed/36285186
http://dx.doi.org/10.1155/2022/1667672
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