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Publication Detail
Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines
  • Publication Type:
    Conference
  • Authors:
    Wottschel V, Ciccarelli O, Chard DT, Miller DH, Alexander DC
  • Publication date:
    15/10/2013
  • Pagination:
    82, 85
  • Published proceedings:
    Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
  • ISBN-13:
    9780769550619
  • Status:
    Published
Abstract
The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE.
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Dept of Computer Science
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IoN - Neuroinflammation
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IoN - Neuroinflammation
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Institute of Neurology
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