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The Decision Support System (DSS) contains more than one antecedent and thedegrees of strength of the antecedents need to be combined to determine theoverall strength of the rule consequent. The membership values of thelinguistic variables in Fuzzy have to be combined using an aggregationoperator. But it is not feasible to predefine the form of aggregation operatorsin decision making. Instead, each rule should be found based on the feeling ofthe experts and on their actual decision pattern over the set of typicalexamples. Thus this work illustrates how the choice of aggregation operators isintended to mimic human decision making and can be selected and adjusted to fitempirical data, a series of test cases. Both parametrized and nonparametrizedaggregation operators are adapted to fit empirical data. Moreover, theyprovided compensatory properties and, therefore, seemed to produce a betterdecision support system. To solve the problem, a threshold point from theoutput of the aggregation operators is chosen as the separation point betweentwo classes. The best achieved accuracy is chosen as the appropriateaggregation operator. Thus a medical decision can be generated which is veryclose to a practitioner's guideline.

On Appropriate Selection of Fuzzy Aggregation Operators in Medical Decision Support System