Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application

Zeeshan Ali; Tahir Mahmood; Hussain AlSalman; Bader Fahad Alkhamees; Sk. Md. Mizanur Rahman; Zeeshan Ali; Tahir Mahmood; Hussain AlSalman; Bader Fahad Alkhamees; Sk. Md. Mizanur Rahman

doi:10.3934/mbe.2022039

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 1: 855-872. doi: 10.3934/mbe.2022039

Previous Article Next Article

Research article Special Issues

Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application

1.
Department of Mathematics & Statistics, International Islamic University Islamabad, Pakistan
2.
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
3.
Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
4.
Information and Communication Engineering Technology, School of Engineering Technology and Applied Science, Centennial College, Toronto, Canada

Received: 30 September 2021 Accepted: 11 November 2021 Published: 22 November 2021

One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts. Likewise, a lot of examples are exposed under the invented measures based on PHF data in the environment of medical diagnosis to demonstrate the stability and elasticity of the explored works. Finally, the sensitive analysis of the presented works is also implemented and illuminated their graphical structures.
- picture hesitant fuzzy sets,
- variation co-efficient similarity measures,
- medical diagnosis
Citation: Zeeshan Ali, Tahir Mahmood, Hussain AlSalman, Bader Fahad Alkhamees, Sk. Md. Mizanur Rahman. Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 855-872. doi: 10.3934/mbe.2022039

Related Papers:

Abstract

One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts. Likewise, a lot of examples are exposed under the invented measures based on PHF data in the environment of medical diagnosis to demonstrate the stability and elasticity of the explored works. Finally, the sensitive analysis of the presented works is also implemented and illuminated their graphical structures.

References

[1]	L. A. Zadeh, Fuzzy sets, Infor. Control, 8 (1965), 338–353. doi.org/10.1142/9789814261302_0021 doi: 10.1142/9789814261302_0021
[2]	J. A. Goguen, L-fuzzy sets, J. Math. Anal. Appl., 18 (1967), 145–174. https://core.ac.uk/download/pdf/82486542.pdf
[3]	Y. Deng, Z. Zhenfu, L. Qi, Ranking fuzzy numbers with an area method using radius of gyration, Comp. Math. Appl., 51 (2006), 1127–1136. https://doi.org/10.1016/j.camwa.2004.11.022 doi: 10.1016/j.camwa.2004.11.022
[4]	K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst., 20 (1986), 87–96. http://dx.doi.org/10.1016/S0165-0114(86)80034-3 doi: 10.1016/S0165-0114(86)80034-3
[5]	D. F. Li, Multiattribute decision making models and methods using intuitionistic fuzzy sets, J. Comp. Syst. Sci., 70 (2005), 73–85. https://doi.org/10.1016/j.jcss.2004.06.002 doi: 10.1016/j.jcss.2004.06.002
[6]	S. K. De, R. Biswas, A. R. Roy, An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Sets Syst., 117 (2001), 209–213. https://doi.org/10.1016/S0165-0114(98)00235-8 doi: 10.1016/S0165-0114(98)00235-8
[7]	L. Dengfeng, C. Chuntian, New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions, Pattern Recogn. Letters, 23 (2002), 221-225. https://doi.org/10.1016/S0167-8655(01)00110-6 doi: 10.1016/S0167-8655(01)00110-6
[8]	B. C. Cuong, Picture fuzzy sets - first results. Part 1, Seminar Neuro-Fuzzy Systems with Applications" Preprint 03/2013 Institute of Mathematics Hanoi, 2013. https://doi.org/10.1109/WICT.2013.7113079 doi: 10.1109/WICT.2013.7113079
[9]	R. Joshi, A new picture fuzzy information measure based on Tsallis–Havrda–Charvat concept with applications in presaging poll outcome, Comput. Appl. Math., 39 (2020), 1–24. https://doi.org/10.1007/s40314-020-1106-z doi: 10.1007/s40314-020-1106-z
[10]	M. Qiyas, S. Abdullah, S. Ashraf, M. Aslam, Utilizing linguistic picture fuzzy aggregation operators for multiple-attribute decision-making problems, Int. J. Fuzzy Syst., 22 (2020), 310–320. https://doi.org/10.1007/s40815-019-00726-7 doi: 10.1007/s40815-019-00726-7
[11]	A. H. Ganie, S. Singh, P. K. Bhatia, Some new correlation coefficients of picture fuzzy sets with applications, Neural Comp. Appl., 32 (2020), 12609–12625. https://doi.org/10.1007/s00521-020-04715-y doi: 10.1007/s00521-020-04715-y
[12]	V. Torra, Hesitant fuzzy sets, Int. J. Int. Syst., 25 (2010), 529–539. https://doi.org/10.1002/int.20418 doi: 10.1002/int.20418
[13]	H. Liao, Z. Xu, X. J. Zeng, Novel correlation coefficients between hesitant fuzzy sets and their application in decision making, Knowledge-Based Syst., 82 (2015), 115–127. https://doi.org/10.1016/j.knosys.2015.02.020 doi: 10.1016/j.knosys.2015.02.020
[14]	Z. Zhang, Hesitant fuzzy power aggregation operators and their application to multiple attribute group decision making, Inform Sci., 234 (2013), 150–181. https://doi.org/10.1016/j.ins.2013.01.002 doi: 10.1016/j.ins.2013.01.002
[15]	D. Li, W. Zeng, J. Li, New distance and similarity measures on hesitant fuzzy sets and their applications in multiple-criteria decision-making, Eng. Appl. Artif. Int., 40 (2015), 11–16. https://doi.org/10.1016/j.engappai.2014.12.012 doi: 10.1016/j.engappai.2014.12.012
[16]	B. Q. Hu, Three-way decision spaces based on partially ordered sets and three-way decisions based on hesitant fuzzy sets, Knowledge-Based Syst., 91 (2016), 16–31. https://doi.org/10.1016/j.knosys.2015.09.026 doi: 10.1016/j.knosys.2015.09.026
[17]	Q. Ding, Y. M. Wang, M. Goh, TODIM dynamic emergency decision-making method based on hybrid weighted distance under probabilistic hesitant fuzzy information, Int. J. Fuzzy Syst., 23 (2021), 474–491. https://doi.org/10.1007/s40815-020-00978-8 doi: 10.1007/s40815-020-00978-8
[18]	I. Beg, T. Rashid, Group decision making using intuitionistic hesitant fuzzy sets, Int. J. Fuzzy Logic Int. Syst., 14 (2014), 181–187. https://doi.org/10.5391/IJFIS.2014.14.3.181 doi: 10.5391/IJFIS.2014.14.3.181
[19]	T. Mahmood, W. Ali, Z. Ali, R. Chinram, Power aggregation operators and similarity measures based on improved intuitionistic hesitant fuzzy sets and their applications to multiple attribute decision making, Comp. Model Eng. Sci., 126 (2021), 1165–1187. https://doi.org/10.32604/cmes.2021.014393 doi: 10.32604/cmes.2021.014393
[20]	K. Ullah, Z. Ali, N. Jan, T. Mahmood, S. Maqsood, Multi-attribute decision making based on averaging aggregation operators for picture hesitant fuzzy sets, Tech. J., 23 (2018), 84–95. https://tj.uettaxila.edu.pk/index.php/technical-journal/article/view/704
[21]	X. Xu, L. Zhang, Q. Wan, A variation coefficient similarity measure and its application in emergency group decision-making, Syst. Eng. Proc., 5 (2012), 119–124. https://doi.org/10.1016/j.sepro.2012.04.019 doi: 10.1016/j.sepro.2012.04.019
[22]	S. Pramanik, P. Biswas, B. C. Giri, Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment, Neural Comp. Appl., 28 (2017), 1163–1176. https://doi.org/10.1007/s00521-015-2125-3 doi: 10.1007/s00521-015-2125-3
[23]	N. Jan, Z. Ali, T. Mahmood, K. Ullah, Some generalized distance and similarity measures for picture hesitant fuzzy sets and their applications in building material recognition and multi-attribute decision making, Punjab Uni. J. Math., 51 (2019), 51–70. http://journals.pu.edu.pk/journals/index.php/pujm/article/viewArticle/3393
[24]	Z. Ahmad, T. Mahmood, M. Saad, N. Jan, K. Ullah, Similarity measures for picture hesitant fuzzy sets and their applications in pattern recognition, J. Prime Res. Math., 15 (2019), 81–100. http://jprm.sms.edu.pk/media/pdf/jprm/volume_15/06.pdf
[25]	M. Riaz, A. Habib, M. J. Khan, P. Kumam, Correlation coefficients for cubic bipolar fuzzy sets with applications to pattern recognition and clustering analysis, IEEE Access, 9 (2021), 109053–109066. https://doi.org/10.1109/ACCESS.2021.3098504 doi: 10.1109/ACCESS.2021.3098504
[26]	P. Liu, M. Akram, A. Sattar, Extensions of prioritized weighted aggregation operators for decision-making under complex q-rung orthopair fuzzy information, J. Int. Fuzzy Syst., 39 (2020), 7469–7493. https://doi.org/10.3233/JIFS-200789 doi: 10.3233/JIFS-200789
[27]	M. J. Khan, P. Kumam, N. A. Alreshidi, W. Kumam, Improved cosine and cotangent function-based similarity measures for q-rung orthopair fuzzy sets and TOPSIS method, Complex Int. Syst., 7 (2021), 2679–2696. https://doi.org/10.1007/s40747-021-00425-7 doi: 10.1007/s40747-021-00425-7
[28]	Y. Rong, Y. Liu, Z. Pei, Complex q‐rung orthopair fuzzy 2‐tuple linguistic Maclaurin symmetric mean operators and its application to emergency program selection, Int. J. Int. Syst., 35 (2020), 1749–1790. https://doi.org/10.1002/int.22271 doi: 10.1002/int.22271
[29]	M. J. Khan, P. Kumam, W. Kumam, Theoretical justifications for the empirically successful VIKOR approach to multi-criteria decision making, Soft Comp., 25 (2021), 7761–7767. https://doi.org/10.1007/s00500-020-05548-6 doi: 10.1007/s00500-020-05548-6
[30]	C. M. Hwang, M. S. Yang, W. L. Hung, New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering, Int. J. Int. Syst., 33 (2018), 1672–1688. https://doi.org/10.1002/int.21990 doi: 10.1002/int.21990
[31]	M. J. Khan, P. Kumam, W. Deebani, W. Kumam, Z. Shah, Bi-parametric distance and similarity measures of picture fuzzy sets and their applications in medical diagnosis, Egyptian Inform. J., 22 (2021), 201–212. https://doi.org/10.1016/j.eij.2020.08.002 doi: 10.1016/j.eij.2020.08.002
[32]	S. Zeng, Y. Hu, & X. Xie, Q-rung orthopair fuzzy weighted induced logarithmic distance measures and their application in multiple attribute decision making, Eng. Appl. Artif. Int., 100 (2021), 104167. https://doi.org/10.1016/j.engappai.2021.104167 doi: 10.1016/j.engappai.2021.104167
[33]	X. Xie, H. Liu, S. Zeng, L. Lin, W. Li, A novel progressively undersampling method based on the density peaks sequence for imbalanced data, Knowledge-Based Syst., 213 (2021), 106689. https://doi.org/10.1016/j.knosys.2020.106689 doi: 10.1016/j.knosys.2020.106689
[34]	Z. Ali, T. Mahmood, M. S. Yang, TOPSIS method based on complex spherical fuzzy sets with Bonferroni mean operators, Mathematics, 8 (2020), 1739–1753. https://doi.org/10.3390/math8101739 doi: 10.3390/math8101739
[35]	S. Zeng, M. Shoaib, S. Ali, F. Smarandache, H. Rashmanlou, F. Mofidnakhaei, Certain properties of single-valued neutrosophic graph with application in food and agriculture organization, Int. J. Comp. Int. Syst., 14 (2021), 1516–1540. https://doi.org/10.2991/ijcis.d.210413.001 doi: 10.2991/ijcis.d.210413.001
[36]	M. J. Khan, J. C. R. Alcantud, P. Kumam, W. Kumam, A. N. Al‐Kenani, An axiomatically supported divergence measure for q‐rung orthopair fuzzy sets, Int. J. Int. Syst., 36 (2021), 6133–6155. https://doi.org/10.1002/int.22545 doi: 10.1002/int.22545
[37]	M. J. Khan, M. I. Ali, P. Kumam, W. Kumam, A. N. Al-Kenani, q-Rung orthopair fuzzy modified dissimilarity measure based robust VIKOR method and its applications in mass vaccination campaigns in the context of COVID-19, IEEE Access, 9 (2021), 93497–93515. DOI: 10.1109/ACCESS.2021.3091179 doi: 10.1109/ACCESS.2021.3091179
[38]	M. J. Khan, P. Kumam, M. Shutaywi, W. Kumam, Improved knowledge measures for q-Rung Orthopair fuzzy sets, Int. J. Comp. Int. Syst., 14 (2021), 1700–1713. https://doi.org/10.2991/ijcis.d.210531.002 doi: 10.2991/ijcis.d.210531.002

Reader Comments

Your name:*

Email:*
© 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)