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Cost Sensitive Hierarchical Classifiers for Non-invasive Recognition of Liver Fibrosis Stage

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)

Abstract

Liver Fibrosis caused by the Hepatitis Virus type C (HCV) may be a serious life-threatening condition if is not diagnosed and treated on time. Our previous research proved that it is possible to estimate liver fibrosis stage in patients with diagnosed HCV only using blood tests. The aim of our research is to find a safe and non-invasive but also inexpensive diagnostic method. As not all blood tests are equally expensive (not only in meaning of money, but also time of analysis), this article introduces a Cost Factor to the hierarchical classifiers. Our classifier has been based on a C4.5 decision tree building algorithm enhanced with a modified EG2 algorithm for maintaining a cost limit.

Keywords

machine learning cost sensitive classification hierarchical classifier decision trees multi-stage pattern recognition medical informatics liver fibrosis 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland
  2. 2.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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