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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Marianne Mueller , Rómer Rosales , Harald Steck , Sriram Krishnan , Bharat Rao , Stefan Kramer

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. More... »

PAGES

119-130

References to SciGraph publications

  • 2006. SD-Map – A Fast Algorithm for Exhaustive Subgroup Discovery in KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2006
  • 2009. Data-Efficient Information-Theoretic Test Selection in ARTIFICIAL INTELLIGENCE IN MEDICINE
  • 2008. Exceptional Model Mining in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 1997. An algorithm for multi-relational discovery of subgroups in PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY
  • Book

    TITLE

    Advances in Intelligent Data Analysis VIII

    ISBN

    978-3-642-03914-0
    978-3-642-03915-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-03915-7_11

    DOI

    http://dx.doi.org/10.1007/978-3-642-03915-7_11

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1042852018


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