Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators

Abstract

A clinically important loss in a person’s understanding, emotive power, or conduct is a symptom of a mental disorder. It generally occurs for genetic, psychological, and/or cognitive reasons and is accompanied by discomfort or limitationin significant functional areas. It can be handled using techniques similar to those used to treat chronic conditions (i.e., precautions, examination, medication, and recovery). Mental diseases take a variety of forms. Mental disorder is also identified as mental illness. The latter is a more usual phrase that incorporates psychological problems, psychosocial disorders, and (other) states of mind linked to considerable discomfort, operational limitations, or danger of loss of sanity. To rank the most prevalent types of mental disorders is a multi-attribute decision-making issue and thus this article aims to analyze the artificial intelligence-based evaluation of mental disorders and rank the most prevalent types of mental disorders. For this purpose, here we invent certain aggregation operators under the environment of the bipolar complex fuzzy set such as bipolar complex fuzzy Schweizer-Sklar prioritized weighted averaging, bipolar complex fuzzy Schweizer-Sklar prioritized ordered weighted averaging, bipolar complex fuzzy Schweizer-Sklar prioritized weighted geometric, bipolar complex fuzzy Schweizer-Sklar prioritized ordered weighted geometric operators. After that, we devise a procedure of decision-making for bipolar complex fuzzy information by employing the introduced operators and then take artificial data in the model of bipolar complex fuzzy set to rank the most prevalent types of mental disorders. Additionally, this article contains a comparative study of the introduced work with a few current works for exhibiting the priority and superiority of the introduced work.