2004 MiningDiagnosticRulesFromClinicalDBs

• (Tsumoto, 2004) ⇒ Shusaku Tsumoto. (2004). “Mining Diagnostic Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model.” In: Information Sciences, 162(2). doi:10.1016/j.ins.2004.03.002.

Subject Headings: Medical Diagnostic Rule, Rough Set, Expert System.

Cited By

• ~67 http://scholar.google.com/scholar?cites=18197541304936970646

2007

• (Pawlak & Skowron, 2007) ⇒ Zdzislaw Pawlak, and Andrzej Skowron. (2007). “Rudiments of Rough Sets.” In: Information Sciences, 177(1). doi:10.1016/j.ins.2006.06.003

Quotes

Author Keywords

Data Mining; Knowledge discovery; Rule induction; Hierarchical decision rules

Abstract

Since rule induction methods generate rules whose lengths are the shortest for discrimination between given classes, they tend to generate rules too short for medical experts. Thus, these rules are difficult for the experts to interpret from the viewpoint of domain knowledge. In this paper, the characteristics of experts' rules are closely examined and a new approach to generate diagnostic rules is introduced. The proposed method focuses on the hierarchical structure of differential diagnosis and consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several generalized groups with respect to the characterization. Then, two kinds of sub-rules, classification rules for each generalized group and rules for each class within each group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts' decision processes.

6. Conclusion

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 MiningDiagnosticRulesFromClinicalDBs	Shusaku Tsumoto			Mining Diagnostic Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model		Library and Information Science Discipline	http://dblab.mgt.ncu.edu.tw/教材/2004 Data Mining/2004-63.pdf	10.1016/j.ins.2004.03.002		2004