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Medical Decision-Making and Pattern Recognition Via an Advanced Similarity Measure Based on Single-Valued Neutrosophic Sets

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Abstract

In the present article, we introduce a new and improved Single-Valued Neutrosophic Sets (SVNSs)-based similarity measure. SVNSs, which are a generalized form of fuzzy sets and intuitionistic fuzzy sets, possess three distinct membership degrees that fully define them: truth, indeterminacy, and falsity. Because SVNSs are highly adept at dealing with uncertainty in a more comprehensive manner, our approach is specifically devised to surpass certain limitations that were unavoidable with previous measures. In order to demonstrate the effectiveness of our proposed measure, we utilize a few counterintuitive examples for validation. Additionally, towards the end of our article, we endeavour to address several practical scenarios in the fields of medical diagnosis and pattern recognition, which fall within the realm of decision-making. We have assigned the preference values as SVNSs, and thereafter, a meticulous comparison is also presented for the outcomes obtained under various methods with the proposed method. The comparative analysis manifests a fair agreement of the final outputs and establishes that the proposed approach is effective as well as feasible. It is important to note that decision-making has consistently been a highly researched area and has experienced significant advancements in recent years. Therefore, our accurate similarity measure and its properties have the potential to significantly contribute to the broader field of neutrosophic decision-making.

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References

  1. Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986;20:87–96.

    Article  Google Scholar 

  2. Biswas P, Pramanik S, Giri BC. A new methodology for neutrosophic multi-attribute decision making with unknown weight information. Neutrosophic Sets Syst. 2014;3:42–52.

    Google Scholar 

  3. Broumi S, Smarandache F. Correlation coefficient of interval neutrosophic set. Appl Mech Mater. 2013;436:511–7.

    Article  Google Scholar 

  4. Broumi S, Smarandache F. Cosine similarity measure of interval neutrosophic sets. Neutrosophic Sets Syst. 2014;5:15–20.

    Google Scholar 

  5. Chi P, Liu P. An extended TOPSIS method for the multi-attribute decision making problems on interval neutrosophic set. Neutrosophic Sets Syst. 2013;1:63–70.

    Google Scholar 

  6. Garg H and Nancy (2017a) Non-linear programming method for multi-criteria decision-making problems under interval neutrosophic set environment. Appl. Intell. 1–15. doi: https://doi.org/10.1007/s10489-017-1070-5

  7. Garg H, Nancy N. Some new biparametric distance measures on single-valued Neutrosophic sets with applications to pattern recognition and medical diagnosis. Information. 2017b;8:162. https://doi.org/10.3390/info8040162.

    Article  Google Scholar 

  8. Grzegorzewski P. Distance between intuitionistic fuzzy sets and / or interval-valued fuzzy sets based on the hausdorff metric. Fuzzy Sets Syst. 2004;148:319–28.

    Article  MathSciNet  Google Scholar 

  9. Huang HL. New distance measure of single-valued neutrosophic sets and its application. Int J Intell Syst. 2016;31:1021–32.

    Article  Google Scholar 

  10. Kong L, Wu Y, Ye J. Misfire fault diagnosis method of gasoline engines using the cosine similarity measure of neutrosophic numbers. Neutrosopic Sets Syst. 2015;8:42–5.

    Google Scholar 

  11. Kumar K, Garg H. TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment. Comput Appl Math. 2016. https://doi.org/10.1007/s40314-016-0402-0.

    Article  Google Scholar 

  12. Liu X. Entropy distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets Syst. 1992;52:305–18.

    Article  MathSciNet  Google Scholar 

  13. Liu P, Wang Y. Multiple attribute decision making method based on single-valued neutrosophic normalized weighted Bonferroni mean. Neural Comput Appl. 2014;25:2001–10.

    Article  Google Scholar 

  14. Liu P, Liu X. The neutrosophic number generalized weighted power averaging operator and its application in multiple attribute group decision making. Int J Mach Learn Cybern. 2016. https://doi.org/10.1007/s13042-016-0508-0.

    Article  Google Scholar 

  15. Majumdar P, Samanta SK. On similarity and entropy of neutrosophic sets. J Intell Fuzzy Syst. 2014;26:1245–52.

    Article  MathSciNet  Google Scholar 

  16. Majumdar P (2015) Neutrosophic Sets and Its Application to Decision Making. In Computational Intelligence for Big Data Analysis: Frontier Advances and Applications; Acharya D, Dehuri S, Eds.; Springer International Publishing: Cham, Switzerland; pp. 97–115.

  17. Nancy N, Garg H. An improved score function for ranking Neutrosophic sets and its application to decision-making process. Int J Uncertain Quantif. 2016;6:377–85.

    Article  Google Scholar 

  18. Peng JJ, Wang JQ, Wang J, Zhang HY, Chen ZH. Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. Int J Syst Sci. 2016;47:2342–58.

    Article  Google Scholar 

  19. Smarandache F, (1998) Neutrosophy. Neutrosophic Probability, Set, and Logic, ProQuest Information & Learning; Infolearnquest: Ann Arbor, MI, USA.

  20. Smarandache F. A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set and Logic; American Research Press; Rehoboth, DE, USA; 1999.

    Google Scholar 

  21. Wang H, Smarandache F, Zhang YQ, Sunderraman R. Single valued neutrosophic sets. Rev Air Force Acad. 2010;1:10–4.

    Google Scholar 

  22. Xu ZS, Yager RR. Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst. 2006;35:417–33.

    Article  MathSciNet  Google Scholar 

  23. Ye J. Multicriteria decision making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst. 2013;42:386–94.

    Article  MathSciNet  Google Scholar 

  24. Ye J. Multiple attribute group decision-making method with completely unknown weights based on similarity measures under single valued neutrosophic environment. J Intell Fuzzy Syst. 2014;27:2927–35.

    Article  MathSciNet  Google Scholar 

  25. Ye J. Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making. Int J Intell Fuzzy Syst. 2014;26:165–72.

    Google Scholar 

  26. Ye J. Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med. 2015;63:171–9.

    Article  Google Scholar 

  27. Ye J. Multiple attribute decision-making method based on the possibility degree ranking method and ordered weighted aggregation operators of interval neutrosophic numbers. J Intell Fuzzy Syst. 2015;28:1307–17.

    Article  MathSciNet  Google Scholar 

  28. Ye J. A netting method for clustering-simplified neutrosophic information. Soft Comput. 2017;21:7571–7.

    Article  Google Scholar 

  29. Zadeh LA. Fuzzy Sets. Inf Control. 1965;8:338–53.

    Article  Google Scholar 

  30. Zhang HY, Wang JQ, Chen XH. Interval neutrosophic sets and their application in multi-criteria decision-making problems. Sci World J. 2014. https://doi.org/10.1155/2014/645953.

    Article  Google Scholar 

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Acknowledgements

The authors greatly acknowledge the support of the DST-PURSE Programme SR/PURSE/2022/143(1).

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  1. Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, 786004, India

    Palash Dutta & Gourangajit Borah

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  1. Palash Dutta
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  2. Gourangajit Borah
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PD and GB jointly proposed the plan of research, GB prepared the manuscript and PD analysed the manuscript for its language and accuracy. Finally, both the authors have read and approved the final manuscript.

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Correspondence to Palash Dutta.

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Dutta, P., Borah, G. Medical Decision-Making and Pattern Recognition Via an Advanced Similarity Measure Based on Single-Valued Neutrosophic Sets. SN COMPUT. SCI. 5, 105 (2024). https://doi.org/10.1007/s42979-023-02429-1

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