Bipolar disorder diagnosis with cubic bipolar fuzzy information using TOPSIS and ELECTRE-I

Bipolar disorder (BD) is a neurodegenerative disease that consists of two main manifestations: mania and depression. In the clinical diagnosis of BD, there are two primary components. First and foremost is itself BD which is often wrongly diagnosed as unipolar depression in clinical finding. This is so because in clinical diagnosis the first. factor is often neglected due to its approach towards positivity. As a result, the element of bipolarity dies down, and the condition worsens. The second disadvantage is that BD is frequently misdiagnosed due to comparable indications and symptoms. To overcome these diagnosis issues, a cubic bipolar fuzzy set (CBFS) which comprises both bipolar fuzzy set (RFS) and interval-valued bipolar fuzzy set (IVBFS) is very helpful for clinical diagnosis of BD. A CBFS has the ability to handle bipolarity with suitable closed intervals to express positive and negative grades to build a solid numerical demonstrating interaction to analyze this disorder effectively. For these objectives, the algorithms of technique for the order of preference by similarity to positive ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE-I) for multi-criteria decision-making (MCDM) based on CBF information are developed. The application of proposed algorithms is presented for clinical diagnosis of BD. In order to discuss efficiency and validity of the proposed MCDM approaches, we apply the authenticity test for ranking of feasible alternatives and optimal decision by TOPSIS and ELECTRE-I. The comparison analysis of proposed MCDM approaches with existing techniques is also given.