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Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

Wottschel, V; Ciccarelli, O; Chard, DT; Miller, DH; Alexander, DC; (2013) Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines. In: PRNI 2013: 3rd International Workshop on Pattern Recognition in NeuroImaging, 22-24 June 2013, Philadelphia, PA, USA. (pp. 82 - 85). IEEE: Piscataway, US. Green open access

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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.

Type: Proceedings paper
Title: Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines
Event: International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2013
Location: Philadelphia, PA, US
Dates: 22 June - 24 June, 2013
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PRNI.2013.30
Publisher version: http://dx.doi.org/10.1109/PRNI.2013.30
Language: English
Additional information: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1415891
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