short-paper

Classification of tumor marker values using heuristic data mining methods

Authors:

Stephan M. Winkler,

Michael Affenzeller,

Herbert StekelAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

Pages 1915 - 1922

https://doi.org/10.1145/1830761.1830826

Published: 07 July 2010 Publication History

Abstract

Tumor markers are substances that are found in blood, urine, or body tissues and that are used as indicators for tumors; elevated tumor marker values can indicate the presence of cancer, but there can also be other causes. We have used a medical database compiled at the blood laboratory of the General Hospital Linz, Austria: Several blood values of thousands of patients are available as well as several tumor markers. We have used several data based modeling approaches for identifying mathematical models for estimating selected tumor marker values on the basis of routinely available blood values; in detail, estimators for the tumor markers AFP, CA-125, CA15-3, CEA, CYFRA, and PSA have been identified and are analyzed in this paper. The documented tumor marker values are classified as "normal" or "elevated"; our goal is to design classifiers for the respective binary classification problems. As we show in the results section, for those medical modeling tasks described here, genetic programming performs best among those techniques that are able to identify nonlinearities; we also see that GP results show less overfitting than those produced using other methods.

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Cited By

Chi Yuan Yongli Wang Yanchao Li (2015)TMDFMProceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2015.7359763(657-660)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/BIBM.2015.7359763
Winkler SAffenzeller MSchaller SStekel HArnold D(2014)Data based prediction of cancer diagnoses using heterogeneous model ensemblesProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2609853(1337-1344)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2609853
Winkler SAffenzeller MKronberger GKommenda MWagner SJacak WStekel H(2014)On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary AlgorithmsAdvanced Methods and Applications in Computational Intelligence10.1007/978-3-319-01436-4_6(95-122)Online publication date: 2014
https://doi.org/10.1007/978-3-319-01436-4_6
Show More Cited By

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Classification of tumor marker values using heuristic data mining methods

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cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

July 2010

1496 pages

ISBN:9781450300735

DOI:10.1145/1830761

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

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Published: 07 July 2010

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GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Chi Yuan Yongli Wang Yanchao Li (2015)TMDFMProceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2015.7359763(657-660)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/BIBM.2015.7359763
Winkler SAffenzeller MSchaller SStekel HArnold D(2014)Data based prediction of cancer diagnoses using heterogeneous model ensemblesProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2609853(1337-1344)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2609853
Winkler SAffenzeller MKronberger GKommenda MWagner SJacak WStekel H(2014)On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary AlgorithmsAdvanced Methods and Applications in Computational Intelligence10.1007/978-3-319-01436-4_6(95-122)Online publication date: 2014
https://doi.org/10.1007/978-3-319-01436-4_6
Winkler SKronberger GAffenzeller MStekel H(2013)Variable Interaction Networks in Medical DataInternational Journal of Privacy and Health Information Management10.4018/ijphim.20130701011:2(1-16)Online publication date: 1-Jul-2013
https://doi.org/10.4018/ijphim.2013070101
Winkler SAffenzeller MStekel HAlba E(2013)Evolutionary identification of cancer predictors using clustered dataProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2466809(1463-1470)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2466809
Winkler SAffenzeller MStekel H(2013)An Integrated Clustering and Classification Approach for the Analysis of Tumor Patient DataRevised Selected Papers of the 14th International Conference on Computer Aided Systems Theory - EUROCAST 2013 - Volume 811110.1007/978-3-642-53856-8_49(388-395)Online publication date: 10-Feb-2013
https://dl.acm.org/doi/10.1007/978-3-642-53856-8_49
Bhowan UJohnston MZhang M(2012)Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced DataIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics10.1109/TSMCB.2011.216714442:2(406-421)Online publication date: 1-Apr-2012
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Winkler SAffenzeller MJacak WStekel HLanzi P(2011)Identification of cancer diagnosis estimation models using evolutionary algorithmsProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002040(503-510)Online publication date: 12-Jul-2011
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Winkler SAffenzeller MKronberger GKommenda MWagner SJacak WStekel H(2011)Analysis of selected evolutionary algorithms in feature selection and parameter optimization for data based tumor marker modelingProceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I10.1007/978-3-642-27549-4_43(335-342)Online publication date: 6-Feb-2011
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