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Mining Diagnostic Taxonomy for Multi-Stage Medical Diagnosis

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Tsumoto, S (2004) Mining Diagnostic Taxonomy for Multi-Stage Medical Diagnosis. In: Pacific Knowledge Acquisition Workshop 2004, 9-10 Aug 2004, Auckland, New Zealand.

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Abstract

Experts' reasoning selects the final diagnosis from many candidates by using hierarchical differential diagnosis. In other words, candidates gives a sophisticated hiearchical taxonomy, usually described as a tree. In this paper, the characteristics of experts' rules are closely examined from the viewpoint of hierarchical 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 one of the similarities between characterization sets. From the relations between similarities, tree-based taxonomy is obtained, which includes enough information for hierarchical diagnosis. The proposed method was evaluated on three medical datasets, the experimental results of which show that induced rules correctly represent experts' decision processes.

Item Type: Conference or Workshop Item (Paper)
Keywords: Rough sets, data mining, taxonomy, granular computing
Publisher: School of Computing, Unviersity of Tasmania
Page Range: pp. 14-28
Date Deposited: 05 Oct 2004
Last Modified: 18 Nov 2014 03:10
URI: http://eprints.utas.edu.au/id/eprint/80
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