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) |
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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.
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/ |
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Catalogue record
Date Deposited: 03 Jul 2013 11:06 |
Last Modified: 18 Dec 2019 15:35 |
Author: | Christo Panchev |
Author: | M N Anwar |
Author: | Michael Oakes |
University Divisions
Faculty of TechnologyFaculty of Technology > School of Computer Science
Subjects
Computing > Artificial IntelligenceActions (login required)
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