Hearing aid classification based on audiology data

Christo Panchev; Mohammad N.  Anwar; Michael Oakes

doi:10.1007/978-3-642-40728-4_47

Hearing aid classification based on audiology data

Christo Panchev, Mohammad N. Anwar, Michael Oakes

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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 language	English
Title of host publication	Proceedings of International Conference on Artificial Neural Networks (ICANN)
Publisher	Springer Verlag
Pages	375-380
Number of pages	6
ISBN (Electronic)	978-3-642-40728-4
ISBN (Print)	978-3-642-40727-7
DOIs	https://doi.org/10.1007/978-3-642-40728-4_47
Publication status	Published - 2013
Externally published	Yes
Event	23rd International Conference on Artificial Neural Networks - Sofia, Bulgaria Duration: 10 Sep 2013 → 13 Sep 2013

Conference

Conference	23rd International Conference on Artificial Neural Networks
Abbreviated title	ICANN 2013
Country/Territory	Bulgaria
City	Sofia
Period	10/09/13 → 13/09/13

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-40728-4_47

Cite this

@inproceedings{a9a7aa18c1d042dfaaf24195c0e25c3a,

title = "Hearing aid classification based on audiology data",

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.",

keywords = "Audiology Data Mining, Decision Support System, Multi-layer Perceptron, Bayesian Network",

author = "Christo Panchev and Anwar, {Mohammad N.} and Michael Oakes",

year = "2013",

doi = "10.1007/978-3-642-40728-4_47",

language = "English",

isbn = "978-3-642-40727-7",

pages = "375--380",

booktitle = "Proceedings of International Conference on Artificial Neural Networks (ICANN)",

publisher = "Springer Verlag",

address = "Austria",

note = "23rd International Conference on Artificial Neural Networks, ICANN 2013 ; Conference date: 10-09-2013 Through 13-09-2013",

}

TY - GEN

T1 - Hearing aid classification based on audiology data

AU - Panchev, Christo

AU - Anwar, Mohammad N.

AU - Oakes, Michael

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Audiology Data Mining

KW - Decision Support System

KW - Multi-layer Perceptron

KW - Bayesian Network

U2 - 10.1007/978-3-642-40728-4_47

DO - 10.1007/978-3-642-40728-4_47

M3 - Conference proceeding

SN - 978-3-642-40727-7

SP - 375

EP - 380

BT - Proceedings of International Conference on Artificial Neural Networks (ICANN)

PB - Springer Verlag

T2 - 23rd International Conference on Artificial Neural Networks

Y2 - 10 September 2013 through 13 September 2013

ER -

Hearing aid classification based on audiology data

Abstract

Conference

Keywords

UN SDGs

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Cite this