SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

•RFE-SVMs predict future outcome of CIS patients with conservative accuracy estimates between 64.9% and 88.1%.•Recursive feature selection improves classification performance compared to using all information.•Relevant features include regional WM lesion load and GM density, as well as the type of C...

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Vydané v:	NeuroImage clinical Ročník 24; s. 102011
Hlavní autori:	Wottschel, Viktor, Chard, Declan T., Enzinger, Christian, Filippi, Massimo, Frederiksen, Jette L., Gasperini, Claudio, Giorgio, Antonio, Rocca, Maria A., Rovira, Alex, De Stefano, Nicola, Tintoré, Mar, Alexander, Daniel C., Barkhof, Frederik, Ciccarelli, Olga
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier Inc 01.01.2019 Elsevier
Predmet:	Adolescent Adult Disease Progression Feature selection Female Follow-Up Studies Gray Matter - diagnostic imaging Humans Image Processing, Computer-Assisted - methods Machine learning classification Magnetic Resonance Imaging - methods Male Middle Aged Multiple sclerosis Multiple Sclerosis - diagnostic imaging Neuroimaging - methods Radiology Regular Retrospective Studies Support Vector Machine White Matter - diagnostic imaging Young Adult Machine learning classification Multiple sclerosis Feature selection
ISSN:	2213-1582, 2213-1582
On-line prístup:	Získať plný text
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