title: Classification of patients on the basis of otoneurological data by using Kohonen networks.
abstract: Machine learning methods such as neural networks, decision trees and genetic algorithms can be useful to aid in the classification of patients. We tested Kohonen artificial neural networks, which are known to be effective for classification tasks. Our sample included patients with six different diseases. The Kohonen network algorithm recognized the four largest groups reliably, but the two smallest groups were too small for the method. Neural networks seem to be promising for the computer-aided classification of otoneurological patients provided that the number of patients used is sufficiently large.
authors.affiliation: Department of Computer and Information Sciences, University of Tampere, Tampere, Finland. mj@cs.uta.fi
authors.forename: M
authors.initials: M
authors.lastname: Juhola
authors.forename: J
authors.initials: J
authors.lastname: Laurikkala
authors.forename: K
authors.initials: K
authors.lastname: Viikki
authors.forename: E
authors.initials: E
authors.lastname: Kentala
authors.forename: I
authors.initials: I
authors.lastname: Pyykkö
mesh_terms: D000465:Algorithms; D006259:Craniocerebral Trauma / Q000453:epidemiology; D003657:Decision Making; D004427:Ear Diseases / Q000145:classification*; D006311:Hearing Disorders / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D008575:Meniere Disease / Q000453:epidemiology; D016571:Neural Networks, Computer; D009464:Neuroma, Acoustic / Q000453:epidemiology; D014717:Vertigo / Q000453:epidemiology