Optimal selection of diagnostic method for diabetes mellitus using complex bipolar fuzzy dynamic data

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.