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Hearing Aid Classification Based on Audiology Data

Panchev, Christo, Anwar, M N and Oakes, Michael (2013) Hearing Aid Classification Based on Audiology Data. In: International Conference on Artificial Neural Networks (ICANN),, 10 – 13 Sep 2013, Sofia, Bulgaria..

Item Type: Conference or Workshop Item (Paper)

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.

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More Information

Depositing User: Glenda Young

Identifiers

Item ID: 3968
URI: http://sure.sunderland.ac.uk/id/eprint/3968
Official URL: http://www.icann2013.org/

Users with ORCIDS

Catalogue record

Date Deposited: 03 Jul 2013 11:06
Last Modified: 18 Dec 2019 15:35

Contributors

Author: Christo Panchev
Author: M N Anwar
Author: Michael Oakes

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science

Subjects

Computing > Artificial Intelligence

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