Enhanced decision-making methods using Complex Bipolar Fuzzy framework to diagnose diabetes mellitus.
The study focuses on establishing effective methods to diagnose diabetes mellitus using Complex Bipolar Fuzzy sets (CBFS) and introduces two novel dynamic aggregation operators—CBF dynamic ordered weighted averaging (CBFDyOWA) and CBF dynamic ordered weighted geometric (CBFDyOWG).
The research team includes multiple authors who contributed to the study but are not individually named.
The date of publication is not specified but reflects the recent advances as discussed.
The research does not specify exact locations, but it discusses methodology applicable to global contexts where diabetes is prevalent.
There is a growing need for accurate detection methods for diabetes, affecting over 422 million people worldwide, necessitating effective screening techniques to prevent severe health complications.
The methodology involves using complex bipolar fuzzy set theory, creating dynamic aggregation operators to assess multiple criteria effectively.
The article highlights the substantial increase in global diabetes cases and the high mortality rate attributed to the disease.
"Dynamic aggregation operators play an important role in decision-making processes by successfully incorporating changes in data over time."
"Our main goal is to improve the accuracy of decision-making processes and the effectiveness of evaluating attributes through innovative methods."
1. Introduction: Introduce the urgent global health issue of diabetes and the need for effective diagnosis methods, incorporating quotes about dynamic aggregation.
2. Background: Provide statistical data on diabetes prevalence and the limitations of current diagnostic methods, leading to the need for new approaches.
3. Methodology and Discovery: Explain the Complex Bipolar Fuzzy framework, detailing how CBFDyOWA and CBFDyOWG work, integrating quotes to highlight their significance.
4. Findings and Implications: Present the comparative effectiveness of the newly developed methods against traditional methods, emphasizing improvement.
5. Conclusion: Summarize the key findings, the innovation brought by the study, and suggest future research directions to bolster diabetes diagnosis accuracy.