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Multi-Aspect Mining In Hepatitis Data

Author

Listed:
  • MUNEAKI OHSHIMA

    (Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, Maebashi 371-0816, Japan)

  • NING ZHONG

    (Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, Maebashi 371-0816, Japan)

  • JUZHEN DONG

    (Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, Maebashi 371-0816, Japan)

  • HIDETO YOKOI

    (School of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa 761-0793, Japan)

Abstract

When therapy using interferon medication for chronic hepatitis patients, various conceptual knowledge/rules will benefit for giving a treatment. The paper describes our work on cooperatively using various data mining agents including GDT-RS, learning with ordered information (LOI), and peculiarity oriented mining (POM) in a spiral discovery process with the multi-phase such as pre-processing, rule mining, and post-processing, for multi-aspect analysis of the hepatitis data and meta learning. GDT-RS is an inductive learning system for discovering decision rules. LOI discovers ordering rules and important features. POM finds peculiarity data/rules. Our methodology and experimental results show that the perspective of medical doctors will be changed from a single type of experimental data analysis towards a holistic view, by using ourmulti-aspect miningapproach.

Suggested Citation

  • Muneaki Ohshima & Ning Zhong & Juzhen Dong & Hideto Yokoi, 2009. "Multi-Aspect Mining In Hepatitis Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 445-472.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:03:n:s0219622009003478
    DOI: 10.1142/S0219622009003478
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