Abstract
Standard patient parameters, tumor markers, and tumor diagnosis records are used for identifying prediction models for tumor markers as well as cancer diagnosis predictions. In this paper we present a hybrid clustering and classification approach that first identifies data clusters (using standard patient data and tumor markers) and then learns prediction models on the basis of these data clusters. The so formed clusters are analyzed and their homogeneity is calculated; the models learned on the basis of these clusters are tested and compared to each other with respect to classification accuracy and variable impacts.
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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Chapman & Hall / CRC (2009)
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)
Davies, D.L., Bouldin, D.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, 224–227 (1979)
Koepke, J.A.: Molecular marker test standardization. Cancer 69, 1578–1581 (1992)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection, pp. 1137–1143. Morgan Kaufmann (1995)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press (1992)
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
MacKay, D.: Information Theory, Inference and Learning Algorithms, pp. 284–292. Cambridge University Press (2003)
Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Gaussian Mixture Models and k-Means Clustering. Cambridge University Press, New York (2007)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Wagner, S.: Heuristic Optimization Software Systems – Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. Ph.D. thesis, Johannes Kepler University Linz (2009)
Winkler, S.: Evolutionary System Identification - Modern Concepts and Practical Applications. Ph.D. thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz (2008)
Winkler, S., Affenzeller, M., Jacak, W., Stekel, H.: Classification of tumor marker values using heuristic data mining methods. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010 (2010)
Winkler, S., Affenzeller, M., Jacak, W., Stekel, H.: Identification of cancer diagnosis estimation models using evolutionary algorithms - a case study for breast cancer, melanoma, and cancer in the respiratory system. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011 (2011)
Winkler, S., Affenzeller, M., Kronberger, G., Kommenda, M., Wagner, S., Dorfer, V., Jacak, W., Stekel, H.: On the use of estimated tumor marker classifications in tumor diagnosis prediction - a case study for breast cancer. Accepted to be published in: International Journal of Simulation and Process Modelling (2013)
Winkler, S., Affenzeller, M., Kronberger, G., Kommenda, M., Wagner, S., Jacak, W., Stekel, H.: On the use of estimated tumor marker classifications in tumor diagnosis prediction - a case study for breast cancer. In: Proceedings of the 23rd European Modeling & Simulation Symposium (2011)
Winkler, S., Affenzeller, M., Stekel, H.: Evolutionary identification of cancer predictors using clustered data - a case study for breast cancer, melanoma, and cancer in the respiratory system. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013 (2013)
Xu, L., Jordan, M.I.: On convergence properties of the EM algorithm for Gaussian mixtures. Neural Computation 8, 129–151 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Winkler, S.M., Affenzeller, M., Stekel, H. (2013). An Integrated Clustering and Classification Approach for the Analysis of Tumor Patient Data. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53856-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-53856-8_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53855-1
Online ISBN: 978-3-642-53856-8
eBook Packages: Computer ScienceComputer Science (R0)