Name
Title | ||
|---|---|---|
Machine Learning Recognition of Otoneurological Patients by Means of the Results of Vestibulo-Ocular Signal Analysis |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CBMS.2008.28 | Jyvaskyla |
Keywords | Field | DocType |
decision trees,diseases,learning (artificial intelligence),medical signal processing,neurophysiology,support vector machines,decision trees,machine learning recognition,otoneurological patients,support vector machines,vestibulo-ocular signal analysis,classification,machine learning,otoneurology,signal analysis,vertigo,vestibulo-ocular reflex | Decision tree,Signal processing,Otoneurology,Neurophysiology,Computer science,Support vector machine,Vestibulo–ocular reflex,Speech recognition,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
1063-7125 | 978-0-7695-3165-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martti Juhola | 1 | 456 | 63.94 |
| Heikki Aalto | 2 | 15 | 4.01 |
| Timo Hirvonen | 3 | 10 | 2.05 |