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

Authors: Stephan M. Winkler; Michael Affenzeller; Gabriel Kronberger; Michael Kommenda; Stefan Wagner; Viktoria Dorfer; Witold Jacak; Herbert Stekel

Addresses: Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media Softwarepark 11, Hagenberg 4232, Austria ' Central Laboratory, General Hospital Linz, Krankenhausstraße 9, Linz 4021, Austria

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.

Keywords: 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; genetic programming; virtual markers.

DOI: 10.1504/IJSPM.2013.055192

International Journal of Simulation and Process Modelling, 2013 Vol.8 No.1, pp.29 - 41

Received: 21 Nov 2011
Accepted: 16 Oct 2012
Published online: 13 Sep 2013 *

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