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Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis

  • Marianne Mueller
  • Rómer Rosales
  • Harald Steck
  • Sriram Krishnan
  • Bharat Rao
  • Stefan Kramer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5772)

Abstract

We propose a new approach to test selection based on the discovery of subgroups of patients sharing the same optimal test, and present its application to breast cancer diagnosis. Subgroups are defined in terms of background information about the patient. We automatically determine the best t subgroups a patient belongs to, and decide for the test proposed by their majority. We introduce the concept of prediction quality to measure how accurate the test outcome is regarding the disease status. The quality of a subgroup is then the best mean prediction quality of its members (choosing the same test for all). Incorporating the quality computation in the search heuristic enables a significant reduction of the search space. In experiments on breast cancer diagnosis data we showed that it is faster than the baseline algorithm APRIORI-SD while preserving its accuracy.

Keywords

Association Rule Breast Cancer Diagnosis Digital Mammography Prediction Quality Prediction Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marianne Mueller
    • 1
  • Rómer Rosales
    • 2
  • Harald Steck
    • 2
  • Sriram Krishnan
    • 2
  • Bharat Rao
    • 2
  • Stefan Kramer
    • 1
  1. 1.Institut für InformatikTechnische Universität MünchenGarchingGermany
  2. 2.IKM CAD and Knowledge SolutionsSiemens Healthcare, MalvernUSA

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