Diagnosis of hypoglycemic episodes using a neural network based rule discovery system

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

@Article{Chan20119799,
  author =       "K. Y. Chan and S. H. Ling and T. S. Dillon and 
                 H. T. Nguyen",
  title =        "Diagnosis of hypoglycemic episodes using a neural
                 network based rule discovery system",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "8",
  pages =        "9799--9808",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.02.020",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-524WF2N-4/2/d9f5c30581fa33cc25387714abbbc4b6",
  keywords =     "genetic algorithms, genetic programming, Neural
                 networks, Hypoglycemic episodes, Medical diagnosis,
                 Type 1 diabetes mellitus",
  abstract =     "Hypoglycemia or low blood glucose is dangerous and can
                 result in unconsciousness, seizures and even death for
                 Type 1 diabetes mellitus (T1DM) patients. Based on the
                 T1DM patients' physiological parameters, corrected QT
                 interval of the electrocardiogram (ECG) signal, change
                 of heart rate, and the change of corrected QT interval,
                 we have developed a neural network based rule discovery
                 system with hybridising the approaches of neural
                 networks and genetic algorithm to identify the
                 presences of hypoglycemic episodes for TIDM patients.
                 The proposed neural network based rule discovery system
                 is built and is validated by using the real T1DM
                 patients' data sets collected from Department of
                 Health, Government of Western Australia. Experimental
                 results show that the proposed neural network based
                 rule discovery system can achieve more accurate results
                 on both trained and unseen T1DM patients' data sets
                 compared with those developed based on the commonly
                 used classification methods for medical diagnosis,
                 statistical regression, fuzzy regression and genetic
                 programming. Apart from the achievement of these better
                 results, the proposed neural network based rule
                 discovery system can provide explicit information in
                 the form of production rules which compensate for the
                 deficiency of traditional neural network method which
                 do not provide a clear understanding of how they work
                 in prediction as they are in an implicit black-box
                 structure. This explicit information provided by the
                 product rules can convince medical doctors to use the
                 neural networks to perform diagnosis of hypoglycemia on
                 T1DM patients.",
}

Genetic Programming entries for Kit Yan Chan Sing Ho Ling Tharam S Dillon Hung Nguyen

Citations