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

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

1 Zitat (Scopus)

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.

Originalsprache	Englisch
Titel	Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
Herausgeber (Verlag)	Springer
Seiten	388-395
Seitenumfang	8
Auflage	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
Publikationsstatus	Veröffentlicht - 2013
Veranstaltung	14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spanien Dauer: 10 Feb. 2013 → 15 Feb. 2013

Publikationsreihe

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

Konferenz

Konferenz	14th International Conference on Computer Aided Systems Theory, Eurocast 2013
Land/Gebiet	Spanien
Ort	Las Palmas de Gran Canaria
Zeitraum	10.02.2013 → 15.02.2013

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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 Aufl., S. 388-395). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8111 LNCS, Nr. 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. Aufl. Springer, 2013. S. 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 Aufl., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Nr. PART 1, Bd. 8111 LNCS, Springer, S. 388-395, 14th International Conference on Computer Aided Systems Theory, Eurocast 2013, Las Palmas de Gran Canaria, Spanien, 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. Aufl. Springer, 2013. S. 388-395 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8111 LNCS, Nr. PART 1).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

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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 Aufl. Springer. 2013. S. 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

Abstract

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