In this paper we describe our analysis of a database of over 180,000 patient records, collected from over 23,000 patients, by the hearing aid clinic at James Cook University Hospital in Middlesbrough, UK. These records consist of audiograms (graphs of the faintest sounds audible to the patient at six different pitches), categorical data (such as age, gender, diagnosis and hearing aid type) and brief free text notes made by the technicians. We mine this data to determine which factors contribute to the decision to fit a BTE (worn behind the ear) hearing aid as opposed to an ITE (worn in the ear) hearing aid. From PCA (principal component analysis) we determined four main audiogram types, and we relate these to the type of hearing aid chosen. We combine the effects of age, gender, diagnosis, masker, mould and individual audiogram frequencies into a single model by means of logistic regression. We also discovered some significant keywords in the free text fields by using the chi-squared (χ2) test, which can also be used in the model. The final model can act a decision support tool to help decide whether an individual patient should be offered a BTE or an ITE hearing aid.