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Title: Data mining of audiology
Author: Anwar, Muhammad Naveed
ISNI:       0000 0004 2736 6934
Awarding Body: University of Sunderland
Current Institution: University of Sunderland
Date of Award: 2012
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This thesis describes the data mining of a large set of patient records from the hearing aid clinic at James Cook University Hospital in Middlesbrough, UK. As typical of medical data in general, these audiology records are heterogeneous, containing the following three different types of data: Audiograms (graphs of hearing ability at different frequencies) Structured tabular data (such as gender, date of birth and diagnosis) Unstructured text (specific observations made about each patient in a free- text or comment field) This audiology data set is unique, as it contains records of patients prescribed with both ITE and BTE hearing aids. ITE hearing aids are not generally available on the British National Health Service in England, as they are more expensive than BTE hearing aids. However, both types of aids are prescribed at James Cook University Hospital in Middlesbrough, UK, which is also an important feature of this data. There are two research questions for this research: Which factors influence the choice of ITE (in the ear) as opposed to BTE (behind the ear) hearing aids? For patients diagnosed with tinnitus (ringing in the ear), which factors influence the decision whether to fit a tinnitus masker (a gentle sound source, worn like a hearing aid, designed to drown out tinnitus)? A number of data mining techniques, such as clustering of audiograms, association analysis of variables (such as, age, gender, diagnosis, masker, mould and free text keywords) using contingency tables and principal component analysis on audiograms were used to find candidate variables to be combined into a decision support system (OSS) where unseen patient records are presented to the system, and the relative likelihood that a patient should be fitted with an ITE as opposed to a BTE aid or a tinnitus with masker as opposed to tinnitus not with masker is returned. The DSS was created using the techniques of logistic regression, Nalve Bayesian analysis and Bayesian network, and these systems were tested using 5 fold cross validations to see which of the techniques produced the better results. The advantage of these techniques for the combination of evidence is that it is easy to see which variables contributed to the final d~~Jpion. The constructed models and the data behind them were validated by"presenting them to the Principal audiologist, Dr. Robertshaw at James Cook University Hospital in Middlesbrough for comments and suggestions for improvements. The techniques developed in this thesis for the construction of prediction models were also used successfully on a different audiology data set from Malaysia. These decisions are typically made by audiology technicians working in the out- patient clinics, on the basis of audiogram results and in consultation with the patients. In many cases, the choice is clear cut, but at other times the technicians might benefit from a second opinion given by an automatic system with an explanation of how that second opinion was arrived at.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available