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Diagnosing Huntington’s Disease Through Gait Dynamics

  • Juliana Paula FelixEmail author
  • Flávio Henrique Teles Vieira
  • Ricardo Augusto Pereira Franco
  • Ronaldo Martins da Costa
  • Rogerio Lopes Salvini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11845)

Abstract

This study proposes an automatic method for identifying Huntington’s disease using features extracted from gait signals derived from force-sensitive resistors. Features were extracted using metrics of fluctuation magnitude and fluctuation dynamics, obtained from a detrended Fluctuation Analysis (DFA). In the classification, five machine learning algorithms (Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Decision Tree (DT)) were compared by the leave-one-out cross-validation method. Our experiments showed that SVM and DT provided the best results, achieving an average accuracy of 100.0%, representing an improvement compared to other results in the literature, and proving the effectiveness of the proposed method.

Keywords

Automatic diagnosis Huntington’s disease Machine learning Gait dynamics 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Instituto de InformáticaUniversidade Federal de GoiásGoiániaBrazil
  2. 2.Escola de Engenharia Elétrica, Mecânica e de ComputaçãoUniversidade Federal de GoiásGoiâniaBrazil

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