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An algorithmic multiple attribute decision-making method for heart problem analysis under neutrosophic hypersoft

Muhammad Ihsan1, Muhammad Saeed1, Agaeb Mahal Alanzi2

  • 1Department of Mathematics, University of Management & Technology, Lahore, Pakistan.

Peerj. Computer Science
|December 11, 2023
PubMed
Summary

This study introduces a fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) for improved medical diagnosis. The novel approach enhances decision-making in complex, uncertain medical data, specifically for heart disease detection.

Keywords:
Decision-makingFuzzy setFuzzy soft expert setHypersoft expert setOptimization algorithm

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Area of Science:

  • Mathematics
  • Computer Science
  • Medical Informatics

Background:

  • Fuzzy parameterized structures offer advanced tools for handling uncertainty.
  • Existing neutrosophic set extensions lack the multi-argument approximation needed for complex data classification.
  • The fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) extends these capabilities by classifying characteristics into sub-characteristic sets.

Purpose of the Study:

  • To introduce and utilize the fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) for medical diagnosis.
  • To adapt Sanchez's method using FpNHse-sets for a more adaptable and dependable decision-making process.
  • To evaluate the efficacy of this combined approach in diagnosing heart disease using real-world data.

Main Methods:

  • Development of the fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) with a multi-argument approximate function.
  • Modification and application of Sanchez's method integrated with FpNHse-sets for medical diagnosis.
  • Implementation and validation using the Cleveland heart disease dataset.

Main Results:

  • The FpNHse-set effectively classifies complex data, enhancing decision-making in uncertain environments.
  • The modified Sanchez's method, combined with FpNHse-sets, shows promising results for heart disease diagnosis.
  • Empirical validation on the Cleveland dataset demonstrates the veracity and potential benefits of the proposed approach.

Conclusions:

  • The integration of FpNHse-sets and modified Sanchez's method offers a robust framework for medical diagnosis, particularly for heart disease.
  • This approach improves adaptability and dependability in decision-making processes involving uncertain medical data.
  • The study highlights the potential of advanced mathematical structures in improving diagnostic accuracy and healthcare outcomes.