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Feature Selection Algorithms Applied to Parkinson’s Disease

  • M. Navío
  • J. J. Aguilera
  • M. J. del Jesus
  • R. González
  • F. Herrera
  • C. Iríbar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2199)

Abstract

In Parkinson’s Disease an analysis of Medical Data could highlight some symptoms, which can be used as a complementary tool in an early diagnosis. This paper analyses some Filter and Wrapper Feature Selection Algorithms and combinations of them that determine some relevant features in relation to this problem. The experimentation carried out with a data set of patients allows us to determine a set of different premorbid personality traits that can be considered in the early diagnosis of Parkinsonism.

Keywords

Feature Selection Feature Subset Near Neighbour Feature Selection Algorithm Premorbid Personality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • M. Navío
    • 1
  • J. J. Aguilera
    • 2
  • M. J. del Jesus
    • 2
  • R. González
    • 3
  • F. Herrera
    • 4
  • C. Iríbar
    • 5
  1. 1.Ramón y Cajal HospitalMadridSpain
  2. 2.Dept. of Computer ScienceUniversity of JaénJaénSpain
  3. 3.Dept. of MedicineUniversity of GranadaGranadaSpain
  4. 4.Dept. of Computer Science and A.I.University of GranadaGranadaSpain
  5. 5.Institute of NeuroscienceUniversity of GranadaGranadaSpain

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