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

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

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Article ID: iaor20135037
Volume: 8
Issue: 1
Start Page Number: 29
End Page Number: 41
Publication Date: Jul 2013
Journal: International Journal of Simulation and Process Modelling
Authors: , , , , , , ,
Keywords: heuristics
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.

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