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Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis
Azrour, Samir; Pierard, Sébastien; Geurts, Pierre et al.
2014In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Peer reviewed
 

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Keywords :
Data Normalization; Machine Learning; Multiple Sclerosis
Abstract :
[en] Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised learning, the condition of patients with multiple sclerosis based on their gait descriptors obtained with a gait analysis system. As the morphological characteristics of individuals influence their gait while being in first approximation independent of the disease level, an original strategy of data normalization with respect to these characteristics is described and applied beforehand in order to obtain more reliable predictions. In addition, we explain how we address the problem of missing data which is a common issue in the field of clinical evaluation. Results show that, based on machine learning combined to the proposed data handling techniques, we can predict a score highly correlated with the condition of patients.
Research center :
INTELSIG
Disciplines :
Electrical & electronics engineering
Author, co-author :
Azrour, Samir ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Pierard, Sébastien ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Van Droogenbroeck, Marc  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis
Publication date :
April 2014
Event name :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Event place :
Bruges, Belgium
Event date :
from 23-04-2014 to 25-04-2014
Audience :
International
Main work title :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Pages :
649-654
Peer reviewed :
Peer reviewed
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
Available on ORBi :
since 31 January 2014

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