A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis

Abstract

Objectives: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respiratory illnesses, posing diagnostic challenges. Therefore, the primary goal of this study is to design a novel decision-support system under a bipolar intuitionistic fuzzy environment to examine an effective TB diagnosing method. Methods: To achieve the aim, a novel fuzzy decision support system is derived by integrating PROMETHEE and ARAS techniques. This technique evaluates TB diagnostic methods under the bipolar intuitionistic fuzzy context. Moreover, the defuzzification algorithm is proposed to convert the bipolar intuitionistic fuzzy score into crisp score. Results: The proposed method found that the sputum test (T3) is the most accurate in diagnosing TB. Additionally, comparative and sensitivity analyses are derived to show the proposed method's efficiency. Conclusion: The proposed bipolar intuitionistic fuzzy sets, combined with the PROMETHEE-ARAS techniques, proved to be a valuable tool for assessing effective TB diagnosing methods.