Hearing aid classification based on audiology data

Christo Panchev, Mohammad N. Anwar, Michael Oakes

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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 publicationProceedings of International Conference on Artificial Neural Networks (ICANN)
PublisherSpringer Verlag
Pages375-380
Number of pages6
ISBN (Electronic)978-3-642-40728-4
ISBN (Print)978-3-642-40727-7
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event23rd International Conference on Artificial Neural Networks - Sofia, Bulgaria
Duration: 10 Sep 201313 Sep 2013

Conference

Conference23rd International Conference on Artificial Neural Networks
Abbreviated titleICANN 2013
CountryBulgaria
CitySofia
Period10/09/1313/09/13

Keywords

  • Audiology Data Mining
  • Decision Support System
  • Multi-layer Perceptron
  • Bayesian Network

Fingerprint Dive into the research topics of 'Hearing aid classification based on audiology data'. Together they form a unique fingerprint.

Cite this