High confidence association rules for medical diagnosis (CROSBI ID 472502)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Gamberger, Dragan ; Lavrač, Nada ; Jovanoski, Viktor
engleski
High confidence association rules for medical diagnosis
This paper elaborates a simple and general decision model based on the so-called confirmation rules. Confirmation rules are generated separately for each diagnostic class so that selected rules cover (and should hence be able to reliably predict) a significant number of cases of the target class. At the same time, a confirmation rule should not cover the cases of non-target diagnostic classes, and when used for prediction it should exclude the possibility of classifying any of the non-target cases into the target class. In this work we have used and tested the association approach for rule generation, accepting only extremely high confidence rules with reasonable support level as potentially good confirmation rules. Experimental results in the problem of coronary artery disease diagnosis illustrate the approach.
inductive learning; association rules; artery disease
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Podaci o prilogu
1999.
objavljeno
Podaci o matičnoj publikaciji
Podaci o skupu
Intelligent Data Analysis in Medicine and Pharmacology IDAMAP'99, a Workshop at the AMIA 1999 Annual Symposium
predavanje
06.11.1999-06.11.1999
Sjedinjene Američke Države