Alolaiyan Hanan, Liaqat Maryam, Razaq Abdul, Shuaib Umer, Baidar Abdul Wakil, Xin Qin
Department of Mathematics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
Division of Science and Technology, Department of Mathematics, University of Education, Lahore, 54770, Pakistan.
Sci Rep. 2025 Jan 31;15(1):3921. doi: 10.1038/s41598-024-84460-7.
Diabetes mellitus refers to a collection of metabolic disorders that affect the way carbohydrates are processed in the body. It is a prominent worldwide health issue. Precise and reliable decision-making methods are critical for identifying the most effective method of detecting diabetes mellitus. This research work highlights the resolution of the aforementioned decision-making scenarios by utilizing dynamic aggregation operators in the complex bipolar fuzzy (CBF) framework. Dynamic aggregation operators, known for their versatility and accuracy, play an important role in decision-making processes by successfully incorporating changes in data over time. The complex bipolar fuzzy set (CBFS) theory is advantageous in efficiently capturing vagueness because it can incorporate extensive problem descriptions that exhibit periodicity and bipolar ambiguity. In this study, we introduce two novel dynamic aggregation operators, namely, the CBF dynamic ordered weighted averaging (CBFDyOWA) operator and the CBF dynamic ordered weighted geometric (CBFDyOWG) operator. In addition, we examine some important characteristics of these operators. We formulate a modified score function to address the shortcomings of the current score function in the CBF settings. Furthermore, we employ these operators to offer a systematic technique to tackle multiple attribute decision-making (MADM) problems using CBF data. We resolve a MADM problem by determining the most appropriate method for diagnosing diabetes mellitus using the developed operators, demonstrating their usefulness in decision-making procedures. Finally, we perform an in-depth comparison to show the stability and dependability of the derived techniques by comparing them with a wide range of their existing counterparts.
糖尿病是指一系列影响人体碳水化合物处理方式的代谢紊乱疾病。它是一个突出的全球健康问题。精确可靠的决策方法对于确定检测糖尿病的最有效方法至关重要。这项研究工作通过在复杂双极模糊（CBF）框架中使用动态聚合算子，突出了上述决策场景的解决方案。动态聚合算子以其通用性和准确性而闻名，通过成功纳入随时间变化的数据，在决策过程中发挥着重要作用。复杂双极模糊集（CBFS）理论在有效捕捉模糊性方面具有优势，因为它可以纳入表现出周期性和双极模糊性的广泛问题描述。在本研究中，我们引入了两种新颖的动态聚合算子，即CBF动态有序加权平均（CBFDyOWA）算子和CBF动态有序加权几何（CBFDyOWG）算子。此外，我们研究了这些算子的一些重要特性。我们制定了一个修改后的得分函数，以解决CBF设置中当前得分函数的缺点。此外，我们使用这些算子提供一种系统的技术，以处理使用CBF数据的多属性决策（MADM）问题。我们通过使用所开发的算子确定诊断糖尿病的最合适方法来解决一个MADM问题，证明了它们在决策程序中的有用性。最后，我们通过将衍生技术与广泛的现有同类技术进行比较，进行了深入比较，以展示其稳定性和可靠性。
