Classification of tumor marker values using heuristic data mining methods

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

  author =       "Stephan M. Winkler and Michael Affenzeller and 
                 Witold Jacak and Herbert Stekel",
  title =        "Classification of tumor marker values using heuristic
                 data mining methods",
  booktitle =    "GECCO 2010 Medical applications of genetic and
                 evolutionary computation (MedGEC)",
  year =         "2010",
  editor =       "Stephen L Smith and Stefano Cagnoni and 
                 Robert Patton",
  isbn13 =       "978-1-4503-0073-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1915--1922",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830761.1830826",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Tumour markers are substances that are found in blood,
                 urine, or body tissues and that are used as indicators
                 for tumors; elevated tumor marker values can indicate
                 the presence of cancer, but there can also be other
                 causes. We have used a medical database compiled at the
                 blood laboratory of the General Hospital Linz, Austria:
                 Several blood values of thousands of patients are
                 available as well as several tumor markers. We have
                 used several data based modelling approaches for
                 identifying mathematical models for estimating selected
                 tumor marker values on the basis of routinely available
                 blood values; in detail, estimators for the tumor
                 markers AFP, CA-125, CA15-3, CEA, CYFRA, and PSA have
                 been identified and are analysed in this paper. The
                 documented tumour marker values are classified as
                 {"}normal{"} or {"}elevated{"}; our goal is to design
                 classifiers for the respective binary classification
                 problems. As we show in the results section, for those
                 medical modeling tasks described here, genetic
                 programming performs best among those techniques that
                 are able to identify nonlinearities; we also see that
                 GP results show less overfitting than those produced
                 using other methods.",
  notes =        "Also known as \cite{1830826} Distributed on CD-ROM at

                 ACM Order Number 910102.",

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