On Parameterized Picture Fuzzy Discriminant Information Measure in Medical Diagnosis Problem

Decision-making processes related to the problems of pattern recognition, clustering, and expert and knowledge-based systems contain a lot of uncertainty in the form of imprecise, incomplete, and inexact information with partial contents where the notions of entropy, discriminant measure and similarity measure play a crucial role. In the present communication, a very recently proposed doubly-parameterized information tool is suitably applied for picture fuzzy sets. This bi-parameterized discriminant measure would give diversification in handling the inexact/incomplete information in terms of obtaining the degree of association and closeness in the data of various applications. Further, the introduced bi-parametric measure has been successfully applied in the principle of minimum discriminant information with the help of some illustrative discussions in the applied fields, e.g., “medical diagnosis”. Additionally, for the validity and efficacy of the presented work, necessary characteristic comparison along with important remarks has been done.