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2008 21st IEEE International Symposium on Computer-Based Medical Systems
Machine Learning Recognition of Otoneurological Patients by Means of the Results of Vestibulo-Ocular Signal Analysis
June 17-June 19
ISBN: 978-0-7695-3165-6
We distinguished a group of otoneurological patients from healthy subjects on the basis of machine learning methods applied to signal analysis results calculated in our earlier research. We classified them to investigate, which methods are the most efficient to separate the two classes from each other. Decision trees and support vector machines yielded the highest average accuracies of 89.8 % and 89.4 % being 1-5 % better than others.
Index Terms:
machine learning, classification, signal analysis, otoneurology, vestibulo-ocular reflex, vertigo
Citation:
Martti Juhola, Heikki Aalto, Timo Hirvonen, "Machine Learning Recognition of Otoneurological Patients by Means of the Results of Vestibulo-Ocular Signal Analysis," cbms, pp.578-580, 2008 21st IEEE International Symposium on Computer-Based Medical Systems, 2008
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