title: 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.
authors.affiliation: Department of Otolaryngology, Jagiellonian University Medical College, ul. Sniadeckich 2, 31-501 Krakow, Poland. asiat@agh.edu.pl
authors.forename: Joanna
authors.initials: J
authors.lastname: Szaleniec
authors.forename: Maciej
authors.initials: M
authors.lastname: Wiatr
authors.forename: Maciej
authors.initials: M
authors.lastname: Szaleniec
authors.forename: Jacek
authors.initials: J
authors.lastname: Składzień
authors.forename: Jerzy
authors.initials: J
authors.lastname: Tomik
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Oleś
authors.forename: Ryszard
authors.initials: R
authors.lastname: Tadeusiewicz
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D010035:Otitis Media, Suppurative / Q000601:surgery*; D012815:Signal Processing, Computer-Assisted; D014433:Tympanoplasty / Q000379:methods*