Genetic programming for knowledge discovery in chest-pain diagnosis

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

  author =       "Celia C. Bojarczuk and Heitor S. Lopes and 
                 Alex A. Freitas",
  title =        "Genetic programming for knowledge discovery in
                 chest-pain diagnosis",
  journal =      "IEEE Engineering in Medicine and Biology Magazine",
  year =         "2000",
  volume =       "19",
  number =       "4",
  pages =        "38--44",
  month =        jul # "-" # aug,
  keywords =     "genetic algorithms, genetic programming, data mining,
                 knowledge discovery, chest-pain diagnosis, predictive
                 accuracy, rule set, comprehensible rules, background
                 knowledge, preprocessing step, data sets, medical
  ISSN =         "0739-5175",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "7 pages",
  abstract =     "Explores a promising data mining approach. Despite the
                 small number of examples available in the authors'
                 application domain (taking into account the large
                 number of attributes), the results of their experiments
                 can be considered very promising. The discovered rules
                 had good performance concerning predictive accuracy,
                 considering both the rule set as a whole and each
                 individual rule. Furthermore, what is more important
                 from a data mining viewpoint, the system discovered
                 some comprehensible rules. It is interesting to note
                 that the system achieved very consistent results by
                 working from {"}tabula rasa,{"} without any background
                 knowledge, and with a small number of examples. The
                 authors emphasize that their system is still in an
                 experiment in the research stage of development.
                 Therefore, the results presented here should not be
                 used alone for real-world diagnoses without consulting
                 a physician. Future research includes a careful
                 selection of attributes in a preprocessing step, so as
                 to reduce the number of attributes (and the
                 corresponding search space) given to the GP. Attribute
                 selection is a very active research area in data
                 mining. Given the results obtained so far, GP has been
                 demonstrated to be a really useful data mining tool,
                 but future work should also include the application of
                 the GP system proposed here to other data sets, to
                 further validate the results reported in this
  notes =        "lilgp",

Genetic Programming entries for Celia Cristina Bojarczuk Heitor Silverio Lopes Alex Alves Freitas