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Diabetes: Symptoms, Diagnosis, and Complications01:15

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

Hanan Alolaiyan1, Maryam Liaqat2, Abdul Razaq2

  • 1Department of Mathematics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.

Scientific Reports
|January 31, 2025
PubMed
Summary

This study introduces new dynamic aggregation operators within the complex bipolar fuzzy framework to improve diabetes mellitus detection. These methods enhance decision-making accuracy for complex health data.

Keywords:
Complex bipolar fuzzy dynamic ordered weighted averaging operatorComplex bipolar fuzzy dynamic ordered weighted geometric operatorComplex bipolar fuzzy setsDiabetes mellitus diagnosisMultiple attribute decision-making

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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.