Abstract
In this paper we report on the use of evolutionary algorithms for optimizing the identification of classification models for selected tumor markers. Our goal is to identify mathematical models that can be used for classifying tumor marker values as normal or as elevated; evolutionary algorithms are used for optimizing the parameters for learning classification models. The sets of variables used as well as the parameter settings for concrete modeling methods are optimized using evolution strategies and genetic algorithms. The performance of these algorithms is analyzed as well as the population diversity progress. In the empirical part of this paper we document modeling results achieved for tumor markers CA 125 and CYFRA using a medical data base provided by the Central Laboratory of the General Hospital Linz; empirical tests are executed using HeuristicLab.
The work described in this paper was done within the Josef Ressel Centre for Heuristic Optimization Heureka! (http://heureka.heuristiclab.com/) sponsored by the Austrian Research Promotion Agency (FFG).
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Winkler, S.M. et al. (2012). Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_43
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DOI: https://doi.org/10.1007/978-3-642-27549-4_43
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