Abstract
In this paper, we summarize our endeavor for spiral discovery of a separate prediction model from chronic hepatitis data. We have initially proposed various learning/discovery methods including time-series decision tree, PrototypeLines, and peculiarity-oriented mining method for mining the data. This experience has motivated us to model physicians as considering typical cases with the specific disease and ruling out clearly exceptional cases. We have developed a spiral discovery system which learns a prediction model for each type of cases, and obtained promising results from experiments.
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Jumi, M., Suzuki, E., Ohshima, M., Zhong, N., Yokoi, H., Takabayashi, K. (2007). Spiral Discovery of a Separate Prediction Model from Chronic Hepatitis Data. In: Sakurai, A., Hasida, K., Nitta, K. (eds) New Frontiers in Artificial Intelligence. JSAI JSAI 2003 2004. Lecture Notes in Computer Science(), vol 3609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71009-7_44
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DOI: https://doi.org/10.1007/978-3-540-71009-7_44
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