Using decision tree induction to model oculomotor data.

Abstract

Decision tree induction is a machine learning method used to generate classification models from data sets. Numerous decision trees were constructed to examine relationships between oculomotor test parameters and lesion sites in a data set containing cases with operated cerebello-pontine angle tumour, operated hemangioblastoma, infarction of cerebello-brainstem and Ménière's disease, and control subjects. The aim was to find useful parameter combinations with discriminatory power. Decision trees constructed using both pursuit eye movements and saccadic eye movements yielded the best classification results. This is reasonable: oculomotor test results vary according to the site of the lesion and so the performance ability of subjects has to be taken into account in the classification. The decision tree program was able to generate classification models from the oculomotor data set. Generated decision trees were intelligible and can be utilized in physicians' research work.