Comparison between Decision Tree and Genetic Programming to distinguish healthy from stroke postural sway patterns

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Marrega:2015:ieeeEMBC,
  author =       "Luiz H. G. Marrega and Simone M. Silva and 
                 Elisangela F. Manffra and Julio C. Nievola",
  booktitle =    "37th Annual International Conference of the IEEE
                 Engineering in Medicine and Biology Society (EMBC)",
  title =        "Comparison between Decision Tree and Genetic
                 Programming to distinguish healthy from stroke postural
                 sway patterns",
  year =         "2015",
  pages =        "6820--6823",
  abstract =     "Maintaining balance is a motor task of crucial
                 importance for humans to perform their daily activities
                 safely and independently. Studies in the field of
                 Artificial Intelligence have considered different
                 classification methods in order to distinguish healthy
                 subjects from patients with certain motor disorders
                 based on their postural strategies during the balance
                 control. The main purpose of this paper is to compare
                 the performance between Decision Tree (DT) and Genetic
                 Programming (GP) - both classification methods of easy
                 interpretation by health professionals - to distinguish
                 postural sway patterns produced by healthy and stroke
                 individuals based on 16 widely used posturographic
                 variables. For this purpose, we used a posturographic
                 dataset of time-series of centre-of-pressure
                 displacements derived from 19 stroke patients and 19
                 healthy matched subjects in three quiet standing tasks
                 of balance control. Then, DT and GP models were trained
                 and tested under two different experiments where
                 accuracy, sensitivity and specificity were adopted as
                 performance metrics. The DT method has performed
                 statistically significant (P <; 0.05) better in both
                 cases, showing for example an accuracy of 72.8percent
                 against 69.2percent from GP in the second experiment of
                 this paper.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/EMBC.2015.7319960",
  ISSN =         "1094-687X",
  month =        aug,
  notes =        "Also known as \cite{7319960}",
}

Genetic Programming entries for Luiz H G Marrega Simone M Silva Elisangela F Manffra Julio C Nievola

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