Intelligent Patient Management

Volume 189 of the series Studies in Computational Intelligence pp 57-70

Machine Learning and Statistical Approaches to Support the Discrimination of Neuro-degenerative Diseases Based on Gait Analysis

  • Huiru ZhengAffiliated withSchool of Computing and Mathematics, University of Ulster
  • , Mingjing YangAffiliated withCollege of physics and Information Engineering, Fuzhou University
  • , Haiying WangAffiliated withSchool of Computing and Mathematics, University of Ulster
  • , Sally McCleanAffiliated withSchool of Computing and Information Engineering, University of Ulster

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Amyotrophic lateral sclerosis, Parkinson’s disease and Huntington’s disease are three neuro-degenerative diseases. In all these diseases, severe disturbances of gait and gait initiation are frequently reported. In this paper, we explore the feasibility of using machine learning and statistical approaches to support the discrimination of these three diseases based on gait analysis. A total of three supervised classification methods, namely support vector machine, KStar and Random Forest, were evaluated on a publicly-available gait dataset. The results demonstrate that it is feasible to apply computational classification techniques in characterise these three diseases with the features extracted from gait cycles. Results obtained show that using selected 4 features based on maximum relevance and minimum redundancy strategy can achieve reasonably high classification accuracy while 5 features can achieve the best performance. The continual increase of the number of features does not significantly improve classification performance.


classification feature selection neuro-degenerative diseases