title: Modelling oculomotor data with decision tree induction.
abstract: We studied relationships between oculomotor test results and the site of lesion in the data set containing patient cases with operated cerebello-pontine angle tumour, operated hemangioblastoma, infarction of cerebello-brainstem or Meniere's disease and control subjects. Several classification models were generated by decision tree induction to find parameter combinations which are efficient in classification. The studied data were characterised by missing values and scarcity of example cases. This study suggested that decision tree induction can be a useful method even in problematic real world classification tasks. Models generated by decision tree induction and evaluated by an expert physician may give information that is beneficial for research.
authors.affiliation: Department of Computer Science, University of Tampere, Finland. kv@cs.uta.fi
authors.forename: K
authors.initials: K
authors.lastname: Viikki
authors.forename: E
authors.initials: E
authors.lastname: Isotalo
authors.forename: M
authors.initials: M
authors.lastname: Juhola
authors.forename: I
authors.initials: I
authors.lastname: Pyykkö
mesh_terms: D020526:Brain Stem Infarctions / Q000175:diagnosis; D002528:Cerebellar Neoplasms / Q000175:diagnosis; D003198:Computer Simulation; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D018325:Hemangioblastoma / Q000175:diagnosis; D006801:Humans; D008575:Meniere Disease / Q000175:diagnosis; D015835:Ocular Motility Disorders / Q000175:diagnosis / Q000209:etiology*