Abstract
Experts’ reasoning in which selects the final diagnosis from many candidates consists of hierarchical differential diagnosis. In other words, candidates gives a sophisticated hiearchical taxonomy, usally described as a tree.
In this paper, the characteristics of experts’ rules are closely examined from the viewpoint of hiearchical decision steps and and a new approach to rule mining with extraction of diagnostic taxonomy from medical datasets is introduced. The key elements of this approach are calculation of the characterization set of each decision attribute (a given class) and the similarities between characterization sets. From the relations between similarities, tree-based taxonomy is obtained, which includes enough information for diagnostic rules.
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Tsumoto, S. (2003). Mining Diagnostic Rules with Taxonomy from Medical Databases. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_7
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DOI: https://doi.org/10.1007/978-3-540-39592-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20256-1
Online ISBN: 978-3-540-39592-8
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