Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitis media patients

Abstract

The application of computer modelling for medical purposes, although challenging, is a promising pathway for further development in the medical sciences. We present predictive neural and k -nearest neighbour (k -NN) models for hearing improvements after middle ear surgery for chronic otitis media . The studied data set comprised 150 patients characterised by the set of input variables: age, gender, preoperative audiometric results, ear pathology and details of the surgical procedure. The predicted (output) variable was the postoperative hearing threshold. The best neural models developed in this study achieved 84% correct predictions for the test data set while the k -NN model produced only 75.8% correct predictions.