On the use of estimated tumour marker classifications in tumour diagnosis prediction - a case study for breast cancer

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Winkler:2013:IJSPM,
  author =       "Stephan M. Winkler and Michael Affenzeller and 
                 Gabriel Kronberger and Michael Kommenda and Stefan Wagner and 
                 Viktoria Dorfer and Witold Jacak and Herbert Stekel",
  title =        "On the use of estimated tumour marker classifications
                 in tumour diagnosis prediction - a case study for
                 breast cancer",
  journal =      "International Journal of Simulation and Process
                 Modelling",
  year =         "2013",
  month =        sep # "~13",
  volume =       "8",
  number =       "1",
  pages =        "29--41",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, medical data analysis, tumour marker
                 modelling, data mining, tumour marker classification,
                 tumour diagnosis prediction, breast cancer, blood
                 parameters, cancer diagnosis, linear regression,
                 k-nearest neighbour, k-nn learning, artificial neural
                 networks, ANNs, support vector machines, SVM, virtual
                 markers.",
  ISSN =         "1740-2131",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  language =     "eng",
  publisher =    "Inderscience Publishers",
  URL =          "http://www.inderscience.com/link.php?id=55192",
  DOI =          "DOI:10.1504/IJSPM.2013.055192",
  abstract =     "In this article, we describe the use of tumour marker
                 estimation models in the prediction of tumour
                 diagnoses. In previous works, we have identified
                 classification models for tumour markers that can be
                 used for estimating tumour marker values on the basis
                 of standard blood parameters. These virtual tumour
                 markers are now used in combination with standard blood
                 parameters for learning classifiers that are used for
                 predicting tumour diagnoses. Several data-based
                 modelling approaches implemented in HeuristicLab have
                 been applied for identifying estimators for selected
                 tumour markers and cancer diagnoses: linear regression,
                 k-nearest neighbour (k-NN) learning, artificial neural
                 networks (ANNs) and support vector machines (SVMs) (all
                 optimised using evolutionary algorithms), as well as
                 genetic programming (GP). We have applied these
                 modelling approaches for identifying models for breast
                 cancer diagnoses; in the results section, we summarise
                 classification accuracies for breast cancer and we
                 compare classification results achieved by models that
                 use measured marker values as well as models that use
                 virtual tumour markers.",
}

Genetic Programming entries for Stephan M Winkler Michael Affenzeller Gabriel Kronberger Michael Kommenda Stefan Wagner Viktoria Dorfer Witold Jacak Herbert Stekel

Citations