Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96069
Title: A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease
Authors: Muthukaruppan, S.
Er, Meng Joo
Keywords: DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
Issue Date: 2012
Series/Report no.: Expert systems with applications
Abstract: This paper presents a particle swarm optimization (PSO)-based fuzzy expert system for the diagnosis of coronary artery disease (CAD). The designed system is based on the Cleveland and Hungarian Heart Disease datasets. Since the datasets consist of many input attributes, decision tree (DT) was used to unravel the attributes that contribute towards the diagnosis. The output of the DT was converted into crisp if–then rules and then transformed into fuzzy rule base. PSO was employed to tune the fuzzy membership functions (MFs). Having applied the optimized MFs, the generated fuzzy expert system has yielded 93.27% classification accuracy. The major advantage of this approach is the ability to interpret the decisions made from the created fuzzy expert system, when compared with other approaches.
URI: https://hdl.handle.net/10356/96069
http://hdl.handle.net/10220/11242
DOI: http://dx.doi.org/10.1016/j.eswa.2012.04.036
Rights: © 2012 Elsevier Ltd.
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
Appears in Collections:EEE Journal Articles

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