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Improving Medical Rule-Based Expert Systems Comprehensibility: Fuzzy Association Rule Mining Approach

Oladipupo, O. O. and Uwadia, C. O. and Ayo, C. K. (2012) Improving Medical Rule-Based Expert Systems Comprehensibility: Fuzzy Association Rule Mining Approach. International Journal of Artificial Intelligence and Soft Computing, 3 (1). pp. 18-29.

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 27 Apr 2017 13:41
Last Modified: 15 Jun 2017 17:54
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/8079

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