Information Measures on Linear Diophantine Fuzzy Soft Sets with Their Applications to the Medical Diagnosis.

Linear Diophantine fuzzy soft set (LDFSS) is a powerful tool to handle uncertain information more flexibly and comprehensively. The aim of this paper is to propose some information measures (similarity measure, distance measure and entropy) for the LDFSS environment. For this aim, we first redefine the notion of LDFSS from a more general perspective. Then, we extend some well-known classical distances such as Hamming distance, Euclidean distance, Minkowski distance and etc. for the LDFSS environment. Also, we give some similarity measures where some of which are constructed from the presented distance measures and introduce the entropy measure for the LDFSSs. After, we construct two different decision-making methods, one based on the presented similarity measures and the other based on the TOPSIS method with entropy. Finally, we demonstrate a numerical example related to the medical diagnosis to illustrate that the proposed methods are more objective and feasible in the applications. [ABSTRACT FROM AUTHOR]