Hearing aid classification based on audiology data

Panchev, Christo; Anwar, Muhammad Naveed and Oakes, Michael Philip (2013). Hearing aid classification based on audiology data. In: Artificial Neural Networks and Machine Learning – ICANN 2013, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg, pp. 375–380.

DOI: https://doi.org/10.1007/978-3-642-40728-4_47

URL: http://link.springer.com/chapter/10.1007%2F978-3-6...

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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About

  • Item ORO ID
  • 41595
  • Item Type
  • Conference or Workshop Item
  • ISBN
  • 3-642-40727-7, 978-3-642-40727-7
  • Academic Unit or School
  • Faculty of Business and Law (FBL)
  • Copyright Holders
  • © 2013 Springer-Verlag Berlin Heidelberg
  • Related URLs
  • Depositing User
  • Muhammad Anwar

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