Classification of Tumor Marker Values Using Heuristic Data Mining Methods

Stephan Winkler, Michael Affenzeller, Witold Jacak, Herbert Stekel

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
PublisherACM Sigevo
Pages1915-1922
Number of pages8
ISBN (Print)9781450300735
DOIs
Publication statusPublished - 2010
EventProceedings of the Genetic and Evolutionary Computation Conference GECCO 2010 - Portland, United States
Duration: 7 Jul 201011 Jul 2010

Publication series

NameProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication

Conference

ConferenceProceedings of the Genetic and Evolutionary Computation Conference GECCO 2010
Country/TerritoryUnited States
CityPortland
Period07.07.201011.07.2010

Keywords

  • Classification
  • Data mining
  • Machine learning
  • Statistical analysis
  • Tumor marker data

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