Mining Diagnostic Rules with Taxonomy from Medical Databases
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.
Unable to display preview. Download preview PDF.
- 2.Everitt, B.S.: Cluster Analysis, 3rd edn. John Wiley & Son, London (1996)Google Scholar
- 4.Quinlan, J.R.: C4.5 – Programs for Machine Learning. Morgan Kaufmann, Palo Alto (1993)Google Scholar
- 5.Shavlik, J.W., Dietterich, T.G. (eds.): Readings in Machine Learning. Morgan Kaufmann, Palo Alto (1990)Google Scholar
- 6.Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory. In: Yager, R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236. John Wiley & Sons, New York (1994)Google Scholar
- 8.Tsumoto, S.: Extraction of Experts’ Decision Rules from Clinical Databases using Rough Set Model. Intelligent Data Analysis 2(3) (1998)Google Scholar
- 9.Tsumoto, S.: Extraction of Hierarchical Decision Rules from Clinical Databases using Rough Sets. Information Sciences (2003) (in print)Google Scholar