Int. J. of Artificial Intelligence and Soft Computing   »   2012 Vol.3, No.1

 

 

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.

 

DOI: 10.1504/IJAISC.2012.048179

 

Int. J. of Artificial Intelligence and Soft Computing, 2012 Vol.3, No.1, pp.29 - 38

 

Available online: 26 Jul 2012

 

 

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