Discovering knowledge from medical databases using evolutionory algorithms

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

@Article{wong:2000:dkm,
  author =       "Man Leung Wong and Wai Lam and Kwong Sak Leung and 
                 Po Shun Ngan and Jack C. Y. Cheng",
  title =        "Discovering knowledge from medical databases using
                 evolutionory algorithms",
  journal =      "IEEE Engineering in Medicine and Biology Magazine",
  year =         "2000",
  volume =       "19",
  number =       "4",
  pages =        "45--55",
  month =        jul # "-" # aug,
  keywords =     "genetic algorithms, genetic programming, database
                 management systems, medical databases, knowledge
                 discovery, Bayesian networks, causality relationship
                 models, Bayesian network learning process, continuous
                 variables, advanced evolutionary algorithms,
                 evolutionary programming, learning tasks, fracture
                 database, child fractures, scoliosis database,
                 scoliosis classification, novel clinical knowledge,
                 database errors",
  ISSN =         "0739-5175",
  URL =          "http://ieeexplore.ieee.org/iel5/51/18543/00853481.pdf",
  size =         "11 pages",
  abstract =     "Discusses learning roles and causal structures for
                 capturing patterns and causality relationships. The
                 authors present their approach for knowledge discovery
                 from two specific medical databases. First, rules are
                 learned to represent the interesting patterns of the
                 data. Second, Bayesian networks are induced to act as
                 causality relationship models among the attributes. The
                 Bayesian network learning process is divided into two
                 phases. In the first phase, a discretization policy is
                 learned to discretize the continuous variables, and
                 then Bayesian network structures are induced in the
                 second phase. The authors employ advanced evolutionary
                 algorithms such as generic genetic programming,
                 evolutionary programming, and genetic algorithms to
                 conduct the learning tasks. From the fracture database,
                 they discovered knowledge about the patterns of child
                 fractures. From the scoliosis database, they discovered
                 knowledge about the classification of scoliosis. They
                 also found unexpected rules that led to discovery of
                 errors in the database. These results demonstrate that
                 the knowledge discovery process can find interesting
                 knowledge about the data, which can provide novel
                 clinical knowledge as well as suggest refinements of
                 the existing knowledge.",
}

Genetic Programming entries for Man Leung Wong Wai Lam Kwong-Sak Leung Po Shun Ngan Jack Chun-yiu Cheng

Citations