Abstract
When therapy using IFN (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 the GDT-RS inductive learning system for discovering decision rules, the LOI (learning with ordered information) for discovering ordering rules and important features, as well as the POM (peculiarity oriented mining) for finding peculiarity data/rules, in a spiral discovery process with multi-phase such as pre-processing, rule mining, and post-processing, for multi-aspect analysis of the hepatitis data and meta learning. 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 our multi-aspect mining approach.
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Ohshima, M., Okuno, T., Fujita, Y., Zhong, N., Dong, J., Yokoi, H. (2005). Spiral Multi-aspect Hepatitis Data Mining. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds) Active Mining. Lecture Notes in Computer Science(), vol 3430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11423270_12
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DOI: https://doi.org/10.1007/11423270_12
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