Article

From a dataset of clinical assessments and gait analysis, this study
was designed to determine which of the assessments or their combinations would
most influence a low gait index
(i.e., severe gait deviations) for individuals
with cerebral palsy. DESIGN: A retrospective search, including clinical and gait
assessments, was conducted from August 2005 to September 2009.
POPULATION: One
hundred and fifty-five individuals with a clinical diagnosis of cerebral palsy
(CP) (mean age (SD): 11 (5.3) years) were selected for the study. METHOD:
Quinlan's Interactive Dichotomizer 3 algorithm for decision-tree induction,
adapted to fuzzy data coding, was employed to predict a Gait Deviation Index
(GDI) from a dataset of clinical assessments (i.e., range of motion, muscle
strength, and level of spasticity). RESULTS: Seven rules that could explain
severe gait deviation (a fuzzy GDI low class) were induced. Overall, the fuzzy
decision-tree method was highly accurate and permitted us to correctly classify
GDI classes 9 out of 10 times using our clinical assessments. CONCLUSION: There
is an important relationship between clinical parameters and gait analysis. We
have identified the main clinical parameters and combinations of these parameters
that lead to severe gait deviations. The strength of the hip extensor, the level
of spasticity and the strength of the tibialis posterior were the most important
clinical parameters for predicting a severe gait deviation.
CI - Copyright (c) 2012 Elsevier B.V. All rights reserved.

Langue : ANGLAIS