Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis

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.

[1]  S. Gold,et al.  Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable , 2008, Multiple sclerosis.

[2]  Marc Van Droogenbroeck,et al.  GAIMS: a powerful gait analysis system satisfying the constraints of clinical routine , 2013 .

[3]  F. Cavillon,et al.  Expanded Disability Status Scale (EDSS) estimation in multiple sclerosis from posturographic data. , 2013, Gait & posture.

[4]  A. B. Drought,et al.  WALKING PATTERNS OF NORMAL MEN. , 1964, The Journal of bone and joint surgery. American volume.

[5]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[6]  T. Stijnen,et al.  Review: a gentle introduction to imputation of missing values. , 2006, Journal of clinical epidemiology.

[7]  Mikel Galar,et al.  Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches , 2013, Knowl. Based Syst..

[8]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[9]  Lisa M Muratori,et al.  Cell phones change the way we walk. , 2012, Gait & posture.

[10]  O. Kwon,et al.  Gender differences in three dimensional gait analysis data from 98 healthy Korean adults. , 2004, Clinical biomechanics.

[11]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[12]  Richard W. Bohannon Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants. , 1997, Age and ageing.

[13]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..