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..

Full text not available from this repository.


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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computing > Artificial Intelligence
Divisions: Digital Innovation Beacon
Digital Innovation Beacon > Computing Workstream
Faculty of Applied Sciences
Faculty of Applied Sciences > Department of Computing Engineering and Technology
Depositing User: Glenda Young
Date Deposited: 03 Jul 2013 11:06
Last Modified: 04 Oct 2013 15:09
URI: http://sure.sunderland.ac.uk/id/eprint/3968

Actions (login required)

View Item View Item