Hearing aid classification based on audiology data

Christo Panchev, Naveed Anwar, Michael Oakes

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)

Abstract

Presented is a comparative study of two machine learning models (MLP Neural Network and Bayesian Network) as part of a decision support system for prescribing ITE (in the ear) and BTE (behind the ear) aids for people with hearing difficulties. The models are developed/trained and evaluated on a large set of patient records from major NHS audiology centre in England. The two main questions which the models aim to address are: 1) What type of hearing aid (ITE/BTE) should be prescribed to the patient? and 2) Which factors influence the choice of ITE as opposed to BTE hearing aids? The models developed here were evaluated against actual prescriptions given by the doctors and showed relatively high classification rates with the MLP network achieving slightly better results.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2013
EditorsValeri Mladenov, Petia Koprinkova-Hristova, Günther Palm, Alessandro E. P. Villa, Bruno Appollini, Nikola Kasabov
Place of PublicationLondon
PublisherSpringer
Pages375-380
Number of pages660
ISBN (Print)9783642407277
Publication statusPublished - 2013
Event23rd International Conference on Artificial Neural Networks - Sofia
Duration: 1 Jan 2013 → …

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Conference

Conference23rd International Conference on Artificial Neural Networks
Period1/01/13 → …

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