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

Area of Science:

• Computational intelligence
• Decision science
• Medical informatics

Background:

• Diabetes mellitus is a global metabolic disorder requiring precise diagnostic methods.
• Existing decision-making techniques struggle with data vagueness and temporal changes.
• Complex bipolar fuzzy sets (CBFS) offer a framework to handle periodicity and bipolar ambiguity.

Purpose of the Study:

• To develop novel dynamic aggregation operators for complex bipolar fuzzy (CBF) environments.
• To introduce a systematic approach for multiple attribute decision-making (MADM) problems with CBF data.
• To enhance the accuracy and reliability of diabetes mellitus detection methods.

Main Methods:

• Introduction of two new operators: CBF dynamic ordered weighted averaging (CBFDyOWA) and CBF dynamic ordered weighted geometric (CBFDyOWG).
• Formulation of a modified score function for CBF settings.
• Application of the operators to solve a MADM problem for diabetes mellitus diagnosis.

Main Results:

• The proposed CBFDyOWA and CBFDyOWG operators effectively handle complex decision-making scenarios.
• A modified score function improves upon existing methods in CBF contexts.
• The developed technique successfully identified the most appropriate method for diabetes mellitus diagnosis.

Conclusions:

• The novel dynamic aggregation operators provide a robust and reliable method for MADM problems in CBF environments.
• The study demonstrates significant improvements in decision-making accuracy for diabetes diagnosis.
• The developed techniques show stability and dependability compared to existing approaches.