Abstract
In this paper, we analyze clinical data to model relationships between clinical data and health levels. During analyses of data, we discovered models which are important for determining health levels but cannot be extracted during machine learning process. We regard such models as chance and propose an interactive determination of such models. The obtained models can be referred to when standard models cannot correctly explain certain individual health levels.
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Abe, A., Hagita, N., Furutani, M., Furutani, Y., Matsuoka, R. (2008). Exceptions as Chance for Computational Chance Discovery. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_93
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DOI: https://doi.org/10.1007/978-3-540-85565-1_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85564-4
Online ISBN: 978-3-540-85565-1
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