Reference : Data normalization and supervised learning to assess the condition of patients with m...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/162592
Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis
English
Azrour, Samir mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Pierard, Sébastien [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Geurts, Pierre [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Van Droogenbroeck, Marc mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Apr-2014
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
649-654
Yes
No
International
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
from 23-04-2014 to 25-04-2014
Bruges
Belgium
[en] Data Normalization ; Machine Learning ; Multiple Sclerosis
[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.
INTELSIG
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals
http://hdl.handle.net/2268/162592

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