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Neutrosophic sets in determining Corona virus.

V Antonysamy1, M Lellis Thivagar2, S Jafari3

  • 1Loyola Degree College (YSRR), Pulivendula 516 390, Andhra Pradesh, India.

Materials Today. Proceedings
|September 8, 2021
PubMed
Summary

This study applies neutrosophic sets to medical data, identifying core patient symptoms using extended Hausdorff minimum distance. This approach aids in determining potential disease types for better medical diagnosis.

Keywords:
03E7203F55Fuzzy setHausdorff minimum distanceIntuitionistic setNeutrosophic set

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Area of Science:

  • Medical Data Analysis
  • Computational Intelligence
  • Decision Support Systems

Background:

  • Medical data analysis often faces challenges with uncertainty and imprecision.
  • Neutrosophic sets offer a robust framework for handling indeterminate and inconsistent information.

Purpose of the Study:

  • To apply neutrosophic set theory to medical datasets.
  • To identify core patient symptoms indicative of specific diseases.
  • To enhance diagnostic capabilities through novel computational methods.

Main Methods:

  • Application of neutrosophic set theory to medical data.
  • Utilizing the extended Hausdorff minimum distance algorithm.
  • Symptom clustering and analysis based on neutrosophic principles.

Main Results:

  • Identification of key patient symptoms through minimum distance calculations.
  • Demonstration of neutrosophic sets' efficacy in medical data analysis.
  • Correlation established between core symptoms and potential disease types.

Conclusions:

  • Neutrosophic sets provide a valuable tool for analyzing complex medical data.
  • The extended Hausdorff minimum distance effectively identifies critical diagnostic symptoms.
  • This methodology offers a promising approach for disease type inference and clinical decision support.