Title: Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach
Authors: Olufunke O. Oladipupo; Charles O. Uwadia; Charles K. Ayo
Addresses: Department of Computer Science and Information Sciences, Covenant University, Ota, Nigeria ' Department of Computer Science, University of Lagos, Lagos, Nigeria ' Department of Computer Science and Information Sciences, Covenant University, Ota, Nigeria
Abstract: In this paper, a Fuzzy Association Rule Mining (FARM) with expert-driven approach is proposed to acquire a knowledge-base, which corresponds more intuitively to human perception with a high comprehensibility. This approach reduces the number of rules in the knowledge-base when compared with the Standard Rule-base Formulation (SRF) and makes possible the rating of the rules according to their relevance. The rule relevance is determined by the measures of significance and certainty factors. The approach is validated using a medical database and the result shows that this approach ultimately reduces the number of rules and enhances the comprehensibility of the expert system.
Keywords: rule-based expert systems; association rule mining; fuzzy logic; CHD; coronary heart disease; knowledge-base acquisition; artificial intelligence; medical expert systems; association rules; data mining.
International Journal of Artificial Intelligence and Soft Computing, 2012 Vol.3 No.1, pp.29 - 38
Received: 08 Sep 2010
Accepted: 04 May 2011
Published online: 29 Nov 2014 *