Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system

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@InProceedings{Winkler:2011:GECCOcomp,
  author =       "Stephan M. Winkler and Michael Affenzeller and 
                 Witold Jacak and Herbert Stekel",
  title =        "Identification of cancer diagnosis estimation models
                 using evolutionary algorithms: a case study for breast
                 cancer, melanoma, and cancer in the respiratory
                 system",
  booktitle =    "GECCO 2011 Medical applications of genetic and
                 evolutionary computation (MedGEC)",
  year =         "2011",
  editor =       "Stephen L. Smith and Stefano Cagnoni and 
                 Robert Patton",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "503--510",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002040",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper we present results of empirical research
                 work done on the data based identification of
                 estimation models for cancer diagnoses: Based on
                 patients' data records including standard blood
                 parameters, tumor markers, and information about the
                 diagnosis of tumours we have trained mathematical
                 models for estimating cancer diagnoses.

                 Several data based modelling approaches implemented in
                 HeuristicLab have been applied for identifying
                 estimators for selected cancer diagnoses: Linear
                 regression, k-nearest neighbour learning, artificial
                 neural networks, and support vector machines (all
                 optimised using evolutionary algorithms) as well as
                 genetic programming. The investigated diagnoses of
                 breast cancer, melanoma, and respiratory system cancer
                 can be estimated correctly in up to 81percent,
                 74percent, and 91percent of the analysed test cases,
                 respectively; without tumour markers up to 75percent,
                 74percent, and 87percent of the test samples are
                 correctly estimated, respectively.",
  notes =        "Also known as \cite{2002040} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Stephan M Winkler Michael Affenzeller Witold Jacak Herbert Stekel

Citations