Variable interaction networks in medical data

Stephan Winkler, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, Stefan Wagner, Witold Jacak, Herbert Stekel

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)


In this paper we describe the identification of variable interaction networks in a medical data set. The main goal is to generate mathematical models for standard blood parameters as well as tumor markers using other available parameters in this data set. For each variable we identify those variables that are most relevant for modeling it; relevance of a variable can in this context be defined via the frequency of its occurrence in models identified by evolutionary machine learning methods or via the decrease in modeling quality after removing it from the data set. Several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected tumor markers and cancer diagnoses: Linear regression and support vector machines (optimized using evolutionary algorithms) as well as genetic programming.

Titel24th European Modeling and Simulation Symposium, EMSS 2012
PublikationsstatusVeröffentlicht - 2012
Veranstaltung24th European Modeling and Simulation Symposium, EMSS 2012 - Vienna, Österreich
Dauer: 19 Sep. 201221 Sep. 2012


Name24th European Modeling and Simulation Symposium, EMSS 2012


Konferenz24th European Modeling and Simulation Symposium, EMSS 2012


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