An algorithm for an improved intuitionistic fuzzy correlation measure with medical diagnostic application

Correlation measure is a vital measuring operator with vast applications in decisionmaking. On the other hand, intuitionistic fuzzy set (IFS) is very resourceful in soft computing to tackle embedded fuzziness in decisionmaking. The extension of correlation measure to intuitionistic fuzzy settinghas proven to be useful in multicriteria decisionmaking (MCDM). This paper introduces a new intuitionistic fuzzy correlation measure encapsulates in an algorithm by taking into account the complete parameters of IFSs. This new computing technique evaluates the strength of relationship and it is defined within the codomain of IFS. The proposed technique is demonstrated with some theoretical results, and numerically authenticated to be superior in terms of performance index in contrast to some existing correlation measures. We demonstrate the application of the new correlation measure coded with JAVA programming language in medical diagnosis to enhance efficiency since diagnosis is a delicate medicaldecisionmaking exercise.