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

Authors:
Joanna Szaleniec;Maciej Wiatr;Maciej Szaleniec;Jacek SkłAdzień;Jerzy Tomik;Krzysztof Ole;Ryszard Tadeusiewicz
Affiliations:
Department of Otolaryngology, Jagiellonian University Medical College, ul. Sniadeckich 2, 31-501 Krakow, Poland;Department of Otolaryngology, Jagiellonian University Medical College, ul. Sniadeckich 2, 31-501 Krakow, Poland;Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, ul. Niezapominajek 8, 30-239 Krakow, Poland;Department of Otolaryngology, Jagiellonian University Medical College, ul. Sniadeckich 2, 31-501 Krakow, Poland;Department of Otolaryngology, Jagiellonian University Medical College, ul. Sniadeckich 2, 31-501 Krakow, Poland;Department of Otolaryngology, Jagiellonian University Medical College, ul. Sniadeckich 2, 31-501 Krakow, Poland;AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
Venue:
Computers in Biology and Medicine
Year:
2013

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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.