Abstract
In this paper, we analyze the procedure of computational medical diagnosis based on collected medical data. Especially, we focus on features or factors which interfere with sufficient medical diagnoses. In order to reduce the data complexity, we introduce medical data categorization. Data are categorized into six categories to be analyzed and to generate rule sets for medical diagnosis. We analyze the relationships among categorized data sets within the context of chance discovery, where hidden or potential relationships lead to improved medical diagnosis. We then suggest the possibility of integrating rule sets derived from categorized data for improving the accuracy of medical diagnosis.
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Abe, A., Hagita, N., Furutani, M., Furutani, Y., Matsuoka, R. (2010). Categorized and Integrated Data Mining of Medical Data from the Viewpoint of Chance Discovery. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15393-8_35
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DOI: https://doi.org/10.1007/978-3-642-15393-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15392-1
Online ISBN: 978-3-642-15393-8
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