TO INTERACTIVE AND TO PREDICT THE DIFFERENT METHODS ASSOCIATION AND DECISION MAKING PROCESS TOOLS

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.