Evolutionary computing for knowledge discovery in medical diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "K. C. Tan and Q. Yu and C. M. Heng and T. H. Lee",
  title =        "Evolutionary computing for knowledge discovery in
                 medical diagnosis",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2003",
  volume =       "27",
  pages =        "129--154",
  number =       "2",
  keywords =     "genetic algorithms, genetic programming, Medical
                 diagnosis, Knowledge discovery, Data mining,
                 Evolutionary computing",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6T4K-47RRWS9-2/2/5c8dfaf6e49d194b0c8ed6e2fd1b5117",
  ISSN =         "0933-3657",
  DOI =          "doi:10.1016/S0933-3657(03)00002-2",
  abstract =     "One of the major challenges in medical domain is the
                 extraction of comprehensible knowledge from medical
                 diagnosis data. a two-phase hybrid evolutionary
                 classification technique is proposed to extract
                 classification rules that can be used in clinical
                 practice for better understanding and prevention of
                 unwanted medical events. In the first phase, a hybrid
                 evolutionary algorithm (EA) is used to confine the
                 search space by evolving a pool of good candidate
                 rules, e.g. genetic programming (GP) is applied to
                 evolve nominal attributes for free structured rules and
                 genetic algorithm (GA) is used to optimise the numeric
                 attributes for concise classification rules without the
                 need of discretisation. These candidate rules are then
                 used in the second phase to optimize the order and
                 number of rules in the evolution for forming accurate
                 and comprehensible rule sets. The proposed evolutionary
                 classifier (EvoC) is validated upon hepatitis and
                 breast cancer datasets obtained from the UCI
                 machine-learning repository. Simulation results show
                 that the evolutionary classifier produces
                 comprehensible rules and good classification accuracy
                 for the medical datasets. Results obtained from t-tests
                 further justify its robustness and invariance to random
                 partition of datasets.",
  notes =        "PMID: 12636976",

Genetic Programming entries for Kay Chen Tan Qi Yu C M Heng Tong Heng Lee