Journal Name:
- Review Of Research
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Abstract (Original Language):
An interactive association rule method has been proposed to improve upon the
quality of the data mining results in large medical datasets. Very often association rule
mining yields numerous meaningless rules. Domain expert can often guide and restrict
the search procedure to contain the number of useless rules. The association rule method
proposed herein is “Apriori” based that takes a target attribute from a clinician to
identify the class of patients in hepatitis database. The clinical data derived from the
electronic healthcare records and the biological information derived from data mining
techniques could provide more imperative input to the decision making process. The aim
of the experiments reported in this chapter was to demonstrate how to mine useful
knowledge hidden in the form of association rules that can help the clinicians to quickly
make sense out of vast clinical datasets.
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