Applicability of a novel Pythagorean fuzzy correlation coefficient in medical diagnosis, clustering, and classification problems

A Pythagorean fuzzy set outperforms fuzzy and intuitionistic fuzzy sets in solving uncertain issues. For comparing Pythagorean fuzzy sets, compatibility indices such as distance, similarity, correlation, divergence, etc. are essential. The sole compatibility metric that reveals the nature of the rel...

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Bibliographic Details
Published inComputational & applied mathematics Vol. 41; no. 8
Main Author Ganie, Abdul Haseeb
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.12.2022
Springer Nature B.V
Subjects
Applications of Mathematics
Applied physics
Classification
Clustering
Compatibility
Computational mathematics
Computational Mathematics and Numerical Analysis
Correlation coefficients
Decision analysis
Decision making
Diagnosis
Fuzzy sets
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematics
Mathematics and Statistics
Medical diagnosis
Pattern analysis
03E72
Pythagorean fuzzy set
68T10
62H30
62H20
Pattern recognition
Medical diagnosis
Clustering
Linguistic hedge
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Summary:A Pythagorean fuzzy set outperforms fuzzy and intuitionistic fuzzy sets in solving uncertain issues. For comparing Pythagorean fuzzy sets, compatibility indices such as distance, similarity, correlation, divergence, etc. are essential. The sole compatibility metric that reveals the nature of the relationship between the Pythagorean fuzzy sets is the correlation coefficient. There are many applications for the correlation coefficient in decision-making, pattern analysis, medical diagnosis, and other areas. The novel coefficient of correlation for Pythagorean fuzzy sets that we have proposed in this study provides both the nature (positive or negative) and level of the correlation between two Pythagorean fuzzy sets. We also compared the proposed correlation coefficient to all previously calculated Pythagorean fuzzy correlation coefficients. The utility of the suggested metrics has been demonstrated in classification, medical diagnosis, and clustering problems employing Pythagorean fuzzy data. All of the Pythagorean fuzzy correlation coefficients that are currently in use have been compared to the findings of the proposed measure. In classification problems, the proposed metric has a high level of confidence.
ISSN:2238-3603
1807-0302
DOI:10.1007/s40314-022-02108-6