UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Predicting outcome in clinically isolated syndrome using machine learning

Wottschel, V; Alexander, DC; Kwok, PP; Chard, DT; Thompson, AJ; Miller, DH; Ciccarelli, O; (2015) Predicting outcome in clinically isolated syndrome using machine learning. NeuroImage , 7 pp. 281-287. 10.1016/j.nicl.2014.11.021. Green open access

[thumbnail of Article] PDF (Article)
1-s2.0-S2213158214001880-main.pdf

Download (560kB)
[thumbnail of Supplementary Table] MS Word (Supplementary Table)
mmc2.docx

Download (16kB)

Abstract

We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient’s lesion features and clinical/demographic characteristics. Seventy-four patients at onset of CIS were scanned and clinically reviewed after one and three years. CDMS was used as the gold standard against which SVM classification accuracy was tested. Radiological features related to lesional characteristics on conventional MRI were defined a priori and used in combination with clinical/demographic features in an SVM.Forward recursive feature elimination with 100 bootstraps and a leave-­one-­out cross‐validation was used to find the most predictive feature combinations. 30 % and 44 % of patients developed CDMS within one and three years, respectively. The SVMs Correctly predicted the presence (or the absence) of CDMS in 71.4 % of patients (sensitivity/specificity: 77 %/66 %) at one year, and in 68 %(60 %/76 %) at three years on average over all bootstraps. Combinations of features consistently gave a higher accuracy in predicting outcome than any single feature. Machine-learning-based classifications can be used to provide an “individualised” prediction of conversion to MS from subjects’ baseline scans and clinical characteristics, with potential to be incorporated into routine clinical practice.

Type: Article
Title: Predicting outcome in clinically isolated syndrome using machine learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.nicl.2014.11.021
Publisher version: http://dx.doi.org/10.1016/j.nicl.2014.11.021
Language: English
Additional information: © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
Keywords: Support vector machines, MRI, Multiple Sclerosis, Clinically Isolated Syndrome
UCL classification: UCL
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/1458870
Downloads since deposit
120Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item