Investigation of rule interestingness in medical data mining

Miho Ohsaki, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


This research experimentally investigates the performance of conventional rule interestingness measures and discusses their usefulness for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected sixteen kinds of interestingness measures for the rules discovered in a dataset on hepatitis. χ2 measure, recall, and accuracy demonstrated the highest performance, and specificity and prevalence did the lowest. The interestingness measures showed a complementary relationship for each other. These results indicated that some interestingness measures have the possibility to predict really interesting rules at a certain level and that the combinational use of interestingness measures will be useful. We then discussed how to combinationally utilize interestingness measures and proposed a post-processing user interface utilizing them, which supports KDD through human-system interaction.

Original languageEnglish
Title of host publicationActive Mining - Second International Workshop, AM 2003, Revised Selected Papers
PublisherSpringer Verlag
Number of pages16
ISBN (Print)3540261575, 9783540261575
Publication statusPublished - 2005
EventSecond International Workshop on Active Mining, AM 2003 - Maebashi, Japan
Duration: 2003 Oct 282003 Oct 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3430 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherSecond International Workshop on Active Mining, AM 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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