Tuberculosis (TB) stands as the second leading global infectious cause of death, following closely behind the impact of COVID-19. The standard approach to diagnose TB involves skin tests, but these tests can yield inaccurate results due to limited access to healthcare and insufficient diagnostic resources. To enhance diagnostic accuracy, this study introduces a novel approach employing a Bipolar Fuzzy Utility Matrix Inference System (BFUMIS) and a Bipolar Mamdani Fuzzy Inference System (BMFIS) to assess TB disease levels. By considering factors associated with the causation of TB, the study devises suitable membership functions for bipolar fuzzy sets (BFS) using both triangular and trapezoidal fuzzy numbers. Using a point factor scale, the study clusters the rules systematically and assesses the level of uncertainty within these grouped rules by utilizing bipolar triangular fuzzy numbers (BTFN). To handle the BTFN, this study proposes converting bipolar triangular fuzzy into bipolar crisp score (CBTFBCS) algorithm as a defuzzification method. The optimal bipolar fuzzy utility sets (BFUS) are determined from the bipolar fuzzy utility matrix to identify patients’ TB disease levels. These sets play a pivotal role in characterizing the severity of TB disease levels in patients. Additionally, rigorous validation of the utility framework is accomplished through measures of bipolar fuzzy satisfactory factors and sensitivity analyses. Furthermore, the study introduces the BMFIS, which presents a novel perspective on the conventional fuzzy inference system. This innovative system integrates the Mamdani fuzzy inference system (MFIS) into a bipolar fuzzy context, enriching the diagnostic process with enhanced insights. To demonstrate the efficacy of the proposed methods, extensive validation is carried out using actual clinical data. The performance metrics used in this validation effectively demonstrate the superiority of the proposed approach.

中文翻译：

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使用双极模糊效用矩阵和双极Mamdani模糊推理系统的肺结核检测智能决策支持系统

结核病 (TB) 是全球第二大传染病死亡原因，紧随 COVID-19 的影响之后。诊断结核病的标准方法包括皮肤测试，但由于获得医疗保健的机会有限和诊断资源不足，这些测试可能会产生不准确的结果。为了提高诊断准确性，本研究引入了一种采用双极模糊效用矩阵推理系统 (BFUMIS) 和双极 Mam​​dani 模糊推理系统 (BMFIS) 来评估结核病水平的新方法。通过考虑与结核病病因相关的因素，该研究使用三角形和梯形模糊数为双极模糊集（BFS）设计了合适的隶属函数。该研究使用点因子量表，系统地对规则进行聚类，并利用双极三角模糊数 (BTFN) 评估这些分组规则内的不确定性水平。为了处理 BTFN，本研究提出将双极三角模糊转换为双极清晰得分（CBTFBCS）算法作为去模糊化方法。根据双极模糊效用矩阵确定最佳双极模糊效用集（BFUS），以识别患者的结核病水平。这些数据集在描述患者结核病严重程度方面发挥着关键作用。此外，通过双极模糊满意因素的测量和敏感性分析完成了效用框架的严格验证。此外，该研究还介绍了 BMFIS，它为传统模糊推理系统提供了一种新颖的视角。这一创新系统将 Mamdani 模糊推理系统 (MFIS) 集成到双极模糊环境中，通过增强的洞察力丰富了诊断过程。为了证明所提出方法的有效性，使用实际临床数据进行了广泛的验证。此验证中使用的性能指标有效地证明了所提出方法的优越性。