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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F13%3A86089191%21RIV14-MSM-27740___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
Over the years, one of the challenges of a rule based expert system is the possibility of evolving a compact and consistent knowledge-base with a fewer numbers of rules that are relevant to the application domain, in order to enhance the comprehensibility of the expert system. In this paper, the hybrid of fuzzy rule mining interestingness measures and fuzzy expert system is exploited as a means of solving the problem of unwieldiness and maintenance complication in the rule based expert system. This negatively increases the knowledge-base space complexity and reduces rule access rate which impedes system response time. To validate this concept, the Coronary Heart Disease risk ratio determination is used as the case study. Results of fuzzy expert system with a fewer numbers of rules and fuzzy expert system with a large numbers of rules are presented for comparison. Moreover, the effect of fuzzy linguistic variable risk ratio is investigated. This makes the expert system recommendation close to human perception. Over the years, one of the challenges of a rule based expert system is the possibility of evolving a compact and consistent knowledge-base with a fewer numbers of rules that are relevant to the application domain, in order to enhance the comprehensibility of the expert system. In this paper, the hybrid of fuzzy rule mining interestingness measures and fuzzy expert system is exploited as a means of solving the problem of unwieldiness and maintenance complication in the rule based expert system. This negatively increases the knowledge-base space complexity and reduces rule access rate which impedes system response time. To validate this concept, the Coronary Heart Disease risk ratio determination is used as the case study. Results of fuzzy expert system with a fewer numbers of rules and fuzzy expert system with a large numbers of rules are presented for comparison. Moreover, the effect of fuzzy linguistic variable risk ratio is investigated. This makes the expert system recommendation close to human perception.
dcterms:title
A FUZZY-MINING APPROACH FOR SOLVING RULE BASED EXPERT SYSTEM UNWIELDINESS IN MEDICAL DOMAIN A FUZZY-MINING APPROACH FOR SOLVING RULE BASED EXPERT SYSTEM UNWIELDINESS IN MEDICAL DOMAIN
skos:prefLabel
A FUZZY-MINING APPROACH FOR SOLVING RULE BASED EXPERT SYSTEM UNWIELDINESS IN MEDICAL DOMAIN A FUZZY-MINING APPROACH FOR SOLVING RULE BASED EXPERT SYSTEM UNWIELDINESS IN MEDICAL DOMAIN
skos:notation
RIV/61989100:27740/13:86089191!RIV14-MSM-27740___
n15:predkladatel
n18:orjk%3A27740
n3:aktivita
n9:P
n3:aktivity
P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
n3:cisloPeriodika
5
n3:dodaniDat
n7:2014
n3:domaciTvurceVysledku
n5:4347269 Abraham Padath, Ajith
n3:druhVysledku
n12:J
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
58627
n3:idVysledku
RIV/61989100:27740/13:86089191
n3:jazykVysledku
n14:eng
n3:klicovaSlova
rule support and confidence; Coronary Heart Disease (CHD); Fuzzy expert system (FES); Expert system (ES)
n3:klicoveSlovo
n10:Expert%20system%20%28ES%29 n10:Coronary%20Heart%20Disease%20%28CHD%29 n10:Fuzzy%20expert%20system%20%28FES%29 n10:rule%20support%20and%20confidence
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[690D6DC814B8]
n3:nazevZdroje
Neural Network World
n3:obor
n19:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
5
n3:projekt
n8:EE.2.3.20.0073 n8:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n7:2013
n3:svazekPeriodika
23
n3:tvurceVysledku
Charles, Uwadia O. Snášel, Václav Abraham Padath, Ajith Charles, Ayo K. Olufunke, Oladipupo O
n3:wos
000328097600004
s:issn
1210-0552
s:numberOfPages
16
n16:organizacniJednotka
27740