Evolving classifiers to inform clinical assessment of Parkinson's disease

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Michael A. Lones and Jane E. Alty and 
                 Stuart E. Lacy and D. R. Stuart Jamieson and Kate L. Possin and 
                 Norbert Schuff and Stephen L. Smith",
  title =        "Evolving classifiers to inform clinical assessment of
                 Parkinson's disease",
  booktitle =    "IEEE Symposium on Computational Intelligence in
                 Healthcare and e-health, CICARE 2013",
  year =         "2013",
  editor_ssci-2013 = "P. N. Suganthan",
  editor =       "Amir Hussain",
  pages =        "76--82",
  address =      "Singapore",
  month =        "16-19 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CICARE.2013.6583072",
  size =         "7 pages",
  abstract =     "We describe the use of a genetic programming system to
                 induce classifiers that can discriminate between
                 Parkinson's disease patients and healthy age-matched
                 controls. The best evolved classifier achieved an AUC
                 of 0.92, which is comparable with clinical diagnosis
                 rates. Compared to previous studies of this nature, we
                 used a relatively large sample of 49 PD patients and 41
                 controls, allowing us to better capture the wide
                 diversity seen within the Parkinson's population.
                 Classifiers were induced from recordings of these
                 subjects' movements as they carried out repetitive
                 finger tapping, a standard clinical assessment for
                 Parkinson's disease. For ease of interpretability, we
                 used a relatively simple window-based classifier
                 architecture which captures patterns that occur over a
                 single tap cycle. Analysis of window matches suggested
                 the importance of peak closing deceleration as a basis
                 for classification. This was supported by a follow-up
                 analysis of the data set, showing that closing
                 deceleration is more discriminative than features
                 typically used in clinical assessment of finger
  notes =        "CICARE 2013

                 also known as \cite{6583072}",

Genetic Programming entries for Michael A Lones Jane E Alty Stuart E Lacy D R Stuart Jamieson Katherine L Possin Norbert Schuff Stephen L Smith