MULTI-ASPECT MINING IN HEPATITIS DATA

Muneaki Ohshima, Ning Zhong (), Juzhen Dong and Hideto Yokoi
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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

International Journal of Information Technology & Decision Making (IJITDM), 2009, vol. 08, issue 03, 445-472

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.

Keywords: Data mining; multi-aspect analysis; hepatitis data; spiral discovery process (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1142/S0219622009003478

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