Variable Interaction Networks in Medical Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Stephan M. Winkler and Gabriel K. Kronberger and 
                 Michael Affenzeller and Herbert Stekel",
  title =        "Variable Interaction Networks in Medical Data",
  journal =      "International Journal of Privacy and Health
                 Information Management (IJPHIM)",
  year =         "2014",
  volume =       "1",
  number =       "2",
  pages =        "1--16",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.4018/ijphim.2013070101",
  abstract =     "In this paper the authors describe the identification
                 of variable interaction networks based on the analysis
                 of medical data. The main goal is to generate
                 mathematical models for medical parameters using other
                 available parameters in this data set. For each
                 variable the authors identify those features that are
                 most relevant for modelling it; the 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 continuous as well
                 as discrete medical variables and cancer diagnoses:
                 Genetic programming, linear regression,
                 k-nearest-neighbour regression, support vector machines
                 (optimized using evolutionary algorithms), and random
                 forests. In the empirical section of this paper the
                 authors describe interaction networks identified for a
                 medical data base storing data of more than 600
                 patients. The authors see that whatever modeling
                 approach is used, it is possible to identify the most
                 important influence factors and display those in
                 interaction networks which can be interpreted without
                 domain knowledge in machine learning or informatics in
  notes =        "Month year also given as July-December 2013",

Genetic Programming entries for Stephan M Winkler Gabriel Kronberger Michael Affenzeller Herbert Stekel