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Publication Detail
Predicting clinically definite multiple sclerosis from onset using SVM
  • Publication Type:
    Conference
  • Authors:
    Kwok PP, Ciccarelli O, Chard DT, Miller DH, Alexander DC
  • Publication date:
    30/11/2012
  • Pagination:
    116, 123
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    7263 LNAI
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
We aim to determine, with the support vector machine (SVM), whether a combination of magnetic resonance imaging (MRI) and demographic features at onset can better predict conversion to clinically definite multiple sclerosis (CDMS) than the single best feature, and which combination is the best predictor. Several lesional features were extracted from MRI scans, e.g., lesion count, lesion load, lesion size distribution, average lesion intensity, average distance of lesions from centre. Other features include type of presentation, age, and expanded disability status scale (EDSS) at onset. With knowledge of further clinical event within one year, we first used individual features and then combinations of features to perform SVM classification by leave-one-out. The best classification accuracies from the two are compared. Combinations of features give higher classification accuracy (77.3%) than the single best feature (61.4%). The best feature combination is: lesion load, average lesion T2 intensity, average distance from centre, shortest horizontal distance from centre, and type of presentation. © 2012 Springer-Verlag.
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Dept of Computer Science
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Neuroinflammation
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Neuroinflammation
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Department of Neuromuscular Diseases
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