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Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in

Katrin Trentzsch1, Paula Schumann2, Grzegorz Śliwiński2

  • 1Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany.

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|August 27, 2021
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Summary

Machine learning effectively identifies gait changes in multiple sclerosis (MS). Different gait analysis systems and algorithms show promise for diagnosing MS and assessing its severity, aiding early intervention.

Keywords:
feature selectiongait analysismachine learningmobilitymultiple sclerosis

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Area of Science:

  • Biomedical Engineering
  • Neurology
  • Data Science

Background:

  • Gait impairment is a primary symptom in multiple sclerosis (MS).
  • Accurate assessment of gait abnormalities requires advanced analysis of multiple gait systems.
  • Early detection of subtle gait changes is crucial for managing MS progression.

Purpose of the Study:

  • To identify the optimal gait analysis system and machine learning algorithm for differentiating between people with MS and healthy controls.
  • To distinguish between people with MS experiencing fatigue and those without.
  • To classify people with MS based on mild versus moderate disability levels.

Main Methods:

  • Utilized data from three gait analysis systems: DIERS pedogait, GAITRite, and Mobility Lab.
  • Applied six machine learning algorithms: Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, rbf, and polynomial kernels.
  • Analyzed data from 54 people with MS and 38 healthy controls.

Main Results:

  • The SVM with an rbf kernel on DIERS data achieved the best performance for healthy-sick classification (κ = 0.49 ± 0.11).
  • The SVM with a linear kernel on GAITRite data demonstrated the highest accuracy in differentiating mild versus moderate MS disability (κ = 0.61 ± 0.06).
  • Machine learning algorithms successfully detected subtle gait alterations.

Conclusions:

  • Machine learning is a viable tool for identifying pathological gait patterns in early-stage MS.
  • The choice of gait analysis system and machine learning algorithm impacts diagnostic accuracy.
  • These findings support the use of advanced computational methods for objective MS gait assessment.