An integrated clustering and classification approach for the analysis of tumor patient data

Stephan Winkler; Michael Affenzeller; Herbert Stekel

doi:10.1007/978-3-642-53856-8_49

An integrated clustering and classification approach for the analysis of tumor patient data

Stephan Winkler, Michael Affenzeller, Herbert Stekel

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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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.

Original language	English
Title of host publication	Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
Publisher	Springer
Pages	388-395
Number of pages	8
Edition	PART 1
ISBN (Print)	978-3-642-53855-1
DOIs	https://doi.org/10.1007/978-3-642-53856-8_49 https://doi.org/10.1007/978-3-642-53856-8-49
Publication status	Published - 2013
Event	14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spain Duration: 10 Feb 2013 → 15 Feb 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8111 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Computer Aided Systems Theory, Eurocast 2013
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	10.02.2013 → 15.02.2013

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Winkler, S., Affenzeller, M., & Stekel, H. (2013). An integrated clustering and classification approach for the analysis of tumor patient data. In Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers (PART 1 ed., pp. 388-395). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8111 LNCS, No. PART 1). Springer. https://doi.org/10.1007/978-3-642-53856-8_49, https://doi.org/10.1007/978-3-642-53856-8-49

Winkler, Stephan ; Affenzeller, Michael ; Stekel, Herbert. / An integrated clustering and classification approach for the analysis of tumor patient data. Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1. ed. Springer, 2013. pp. 388-395 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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Winkler, S , Affenzeller, M & Stekel, H 2013, An integrated clustering and classification approach for the analysis of tumor patient data. in Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8111 LNCS, Springer, pp. 388-395, 14th International Conference on Computer Aided Systems Theory, Eurocast 2013, Las Palmas de Gran Canaria, Spain, 10.02.2013. https://doi.org/10.1007/978-3-642-53856-8_49, https://doi.org/10.1007/978-3-642-53856-8-49

An integrated clustering and classification approach for the analysis of tumor patient data. / Winkler, Stephan ; Affenzeller, Michael; Stekel, Herbert.
Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1. ed. Springer, 2013. p. 388-395 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8111 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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AB - 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.

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Winkler S , Affenzeller M, Stekel H. An integrated clustering and classification approach for the analysis of tumor patient data. In Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers. PART 1 ed. Springer. 2013. p. 388-395. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-53856-8_49, 10.1007/978-3-642-53856-8-49

An integrated clustering and classification approach for the analysis of tumor patient data

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