Evolving rule-based systems in two medical domains using genetic programming

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

@Article{Tsakonas:2004:AIM,
  author =       "Athanasios Tsakonas and Georgios Dounias and 
                 Jan Jantzen and Hubertus Axer and Beth Bjerregaard and 
                 Diedrich {Graf von Keyserlingk}",
  title =        "Evolving rule-based systems in two medical domains
                 using genetic programming",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2004",
  volume =       "32",
  pages =        "195--216",
  number =       "3",
  abstract =     "Summary Objective: To demonstrate and compare the
                 application of different genetic programming (GP) based
                 intelligent methodologies for the construction of
                 rule-based systems in two medical domains: the
                 diagnosis of aphasia's subtypes and the classification
                 of pap-smear examinations.

                 Material:

                 Past data representing (a) successful diagnosis of
                 aphasia's subtypes from collaborating medical experts
                 through a free interview per patient, and (b) correctly
                 classified smears (images of cells) by
                 cyto-technologists, previously stained using the
                 Papanicolaou method.

                 Methods:

                 Initially a hybrid approach is proposed, which combines
                 standard genetic programming and heuristic hierarchical
                 crisp rule-base construction. Then, genetic programming
                 for the production of crisp rule based systems is
                 attempted. Finally, another hybrid intelligent model is
                 composed by a grammar driven genetic programming system
                 for the generation of fuzzy rule-based
                 systems.

                 Results:

                 Results denote the effectiveness of the proposed
                 systems, while they are also compared for their
                 efficiency, accuracy and comprehensibility, to those of
                 an inductive machine learning approach as well as to
                 those of a standard genetic programming symbolic
                 expression approach.

                 Conclusion:

                 The proposed GP-based intelligent methodologies are
                 able to produce accurate and comprehensible results for
                 medical experts performing competitive to other
                 intelligent approaches. The aim of the authors was the
                 production of accurate but also sensible decision rules
                 that could potentially help medical doctors to extract
                 conclusions, even at the expense of a higher
                 classification score achievement.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6T4K-4DPSHH7-1/2/621e877a6e662298c25372811ae23041",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Hybrid
                 intelligence, Grammar driven GP, Genetic-fuzzy systems,
                 Inductive machine learning, Medical decision making,
                 Aphasia, Pap-smear test",
  DOI =          "doi:10.1016/j.artmed.2004.02.007",
  notes =        "cites \cite{Tsakonas:2001:EUNITEa} and
                 \cite{Tsakonas:2001:EUNITEpap} PMID: 15531151",
}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias Jan Jantzen Hubertus Axer Beth Bjerregaard Diedrich Graf von Keyserlingk

Citations