Hearing aid classification based on audiology data

Panchev, Christo, Anwar, Naveed and Oakes, Michael (2013) Hearing aid classification based on audiology data. In: Artificial Neural Networks and Machine Learning – ICANN 2013. Lecture Notes in Computer Science (8131). Springer, London, pp. 375-380. ISBN 9783642407277

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Official URL: http://dx.doi.org/10.1007/978-3-642-40728-4_47

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.

Item Type: Book Section
Uncontrolled Keywords: audiology Data Mining, decision support system, multi-layer perceptron Bayesian Network
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Mathematics and Information Sciences
Depositing User: Naveed Anwar
Date Deposited: 24 Apr 2015 13:42
Last Modified: 10 Nov 2016 12:40
URI: http://nrl.northumbria.ac.uk/id/eprint/22199

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