Diagnosis of Depression Disorder Using Complex Non-Linear Diophantine Fuzzy Information

Abstract

Depression is recognized as a persistent mood illness. We develop a mathematical model of how clinically expert's psychiatrists identify signs of depression and provide a diagnosis. No matter what kind of therapy you select, psychotherapy should be a pleasant and supportive experience. You should always feel confident discussing your emotions and problems with depression when dealing with a psychotherapist. Five depression therapy techniques have been considered into a multi-criteria group decision-making (MCGDM) issue applying the Dombi geometric operator combining complex non-linear Diophantine fuzzy (CN-LDF) information. In real-world decision-making situations, most issues involve many factors that have to be considered. It may often be difficult to make decisions in these kinds of situations. We will need to use MCGDM procedures to prepare for such complex challenges. This study will provide a new approach called COmbinative Distance-Based Assessment (CODAS) and explore MCGDM problems. In this work, we developed new operators: CN-LDF Dombi weighted geometric (CN-LDFDWG), CN-LDF Dombi order weighted geometric (CN-LDFDOWG), and CN-LDF Dombi hybrid weighted geometric (CN-LDFDHWG). Further, the proposed model is implemented to solve a real-life problem related to the treatment therapy for depression disorder under CN-LDF Dombi Norms. The developed operators are more effective and reliable as compared to the existing operators.