SDI: Shape distribution indicator and its application to find interrelationships between physical activity tests and other medical measures

Ashkan Sami, Ryoichi Nagatomi, Makoto Takahashi, Takeshi Tokuyama

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

Abstract

Comprehensibility is driving force in medical data mining results since doctors utilize the outputs and give the final decision. Another important issue specific to some data sets, like physical activity, is their uniform distribution due to tile analysis that was performed on them In this paper, we propose a novel data mining tool named SDI (Shape Distribution Indicator) to give a comprehensive view of co-relations of attributes together with an index named ISDI to show the robustness of SDI outputs. We apply SDI to explore the relationship of the Physical Activity data and symptoms in medical test dataset for which popular data mining methods fail to give an appropriate output to help doctors decisions. In our experiment, SDI found several useful relationships. 1 Introductio.

Original languageEnglish
Title of host publicationAI 2006
Subtitle of host publicationAdvances in Artificial Intelligence - 19th Australian Joint Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Pages383-392
Number of pages10
ISBN (Print)9783540497875
DOIs
Publication statusPublished - 2006
Event19th Australian Joint Conference onArtificial Intelligence, AI 2006 - Hobart, TAS, Australia
Duration: 2006 Dec 42006 Dec 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4304 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th Australian Joint Conference onArtificial Intelligence, AI 2006
CountryAustralia
CityHobart, TAS
Period06/12/406/12/8

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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