An application of fuzzy bipolar weighted correlation coefficient in decision-making problem.

Medical diagnosis involves interpreting signs, symptoms, and tests. Skin disorders present unique challenges due to overlapping symptoms. Traditional methods struggle with bipolar symptoms—those that can be both positive and negative. Prior research has shown that fuzzy sets can model uncertainty in diagnostic data. However, no prior work had resolved how to effectively integrate bipolarity into diagnostic models. This gap motivated the use of bipolar fuzzy sets. These sets allow for dual assessments of symptoms. Yet, applying them in decision-making remains underexplored. This paper introduces a novel approach to model diagnostic uncertainty.

The aim of this study is to develop a decision-making framework for skin disorder diagnosis. The challenge lies in handling symptoms with bipolar characteristics. The authors propose using bipolar fuzzy sets to capture both positive and negative symptom aspects. They combine this with soft sets to enhance precision in diagnostic models. The goal is to create a more accurate decision-making tool. This approach addresses the ambiguity in symptom interpretation. The study also evaluates the effectiveness of the proposed method. The results are compared with existing strategies to validate improvements.

The study introduces a new decision-making method using bipolar fuzzy soft sets. It begins by modeling symptoms with bipolar fuzzy sets. These sets are then combined with soft sets to refine data representation. A correlation coefficient is calculated to measure relationships between symptoms. The weighted correlation coefficient is also proposed to prioritize symptoms. Numerical computations are used to test the proposed method. Existing strategies are compared to assess performance. The framework is applied to a skin disorder diagnosis scenario.

The proposed method successfully handles bipolar symptoms in diagnostic data. The correlation coefficient for bipolar fuzzy soft sets was computed. Weighted coefficients were shown to improve diagnostic accuracy. Numerical examples demonstrated the method's effectiveness. The results outperformed existing strategies in some cases. Symptom prioritization using weights enhanced decision precision. The framework reduced ambiguity in symptom interpretation. The study confirmed the method's applicability in medical diagnosis.

The authors propose that bipolar fuzzy sets can model diagnostic uncertainty effectively. The new decision-making method improves accuracy in skin disorder diagnosis. Weighted correlation coefficients enhance symptom prioritization. The study confirms that the proposed framework is more precise than existing strategies. The results suggest that bipolar fuzzy sets are suitable for handling dual symptoms. The approach provides a structured way to interpret ambiguous diagnostic data. The method's implementation was validated through numerical examples. The findings support the use of bipolar fuzzy sets in medical decision-making.