On the use of estimated tumour marker classifications in tumour diagnosis prediction - a case study for breast cancer Online publication date: Mon, 22-Jul-2013
by Stephan M. Winkler; Michael Affenzeller; Gabriel Kronberger; Michael Kommenda; Stefan Wagner; Viktoria Dorfer; Witold Jacak; Herbert Stekel
International Journal of Simulation and Process Modelling (IJSPM), Vol. 8, No. 1, 2013
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.
Online publication date: Mon, 22-Jul-2013
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