Confirmation rule induction and its applications to coronary heart disease diagnosis and risk group discovery

Issue title: Challenges for future intelligent systems in biomedicine

Article type: Research Article

Authors: Gamberger, Dragan | Lavrač, Nada | Krstačić, Goran

Affiliations: Rudjer Bošković Institute, Bijenička 54, 10000 Zagreb, Croatia. Tel.: +385 1 456 1142; Fax: +385 1 468 0114; E-mail:~dragan.gamberger@irb.hr | Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia. E-mail: nada.lavrac@ijs.si | Institute for Cardiovascular Prevention and Rehabilitation, Draškovićeva 13, 10000 Zagreb, Croatia. E-mail: goran.krstacic@zg.hinet.hr

Note: [] Corresponding author

Abstract: The confirmation rule set concept, presented in this paper, provides a framework for reliable decision making. This framework enables flexible formation of a confirmation rule set, by incorporating rules induced by machine learning algorithms as well as human encoded expert rules. The only conditions for including a rule into the confirmation rule set are its high predictive accuracy and relative independence from other rules in the rule set. This paper introduces the concept of confirmation rule sets, together with an algorithm for selecting relatively independent rules from a set of acceptable confirmation rules. It presents also two approaches to confirmation rule induction: first, an exhaustive confirmation rule construction algorithm that was used to discover diagnostic rules in the coronary heart disease diagnosis problem, and second, a heuristic confirmation rule construction algorithm that was used for subgroup discovery in the coronary heart disease risk group detection problem.

Journal: Journal of Intelligent & Fuzzy Systems, vol. 12, no. 1, pp. 35-48, 2002