Enhancing Diabetes Diagnosis through an Intuitionistic Fuzzy Soft Matrices-Based Algorithm
DOI:
https://doi.org/10.31181/sems1120238uKeywords:
Decision-Making, Soft Set, IFS, IFSMs, Complement of IFSMs, Product of the IFSMAbstract
The diagnosis of Type-1 diabetes is a challenging and sophisticated procedure for medical experts. The complexity of this condition necessitates the use of sophisticated decision-making tools, and in this setting, intuitionistic soft set theory and its accompanying matrices prove to be invaluable resources. Our suggested technique uses intuitionistic soft matrices to solve challenging multi-criteria decision-making issues, opening a promising new direction for improving the precision of diabetes diagnosis. Diabetes requires careful evaluation and assessment since it is characterized by several illnesses that interfere with the body's capacity to control blood plasma glucose levels. Our main goal is to use intuitionistic fuzzy soft matrices to thoroughly examine diabetic patients in the decision-making domain. By giving medical professionals more accurate tools to treat this pervasive and difficult health issue, this novel strategy has the potential to revolutionize the diagnosis and management of diabetes.
Downloads
References
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8, 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning I. Information Science, 8, 199–249. https://doi.org/10.1016/0020-0255(75)90036-5.
Pawlak, Z. (1982). Rough Sets. International Journal of Computer and Information Science, 11, 341-356.
Antanassov, K. (1986). Intuitionistic fuzzy set. Fuzzy Sets and Systems, 20(1), 87-96.
Gerstenkorn, T., & Mańko, J. (1991). Correlation of intuitionistic fuzzy sets. Fuzzy Sets and Systems, 44(1), 39-43. https://doi.org/10.1016/0165-0114(91)90031-K.
Molodtsov, D. (1999). Soft Set Theory-First Results. Computers and Mathematics with Applications, 37, 19-31. https://doi.org/10.1016/S0898-1221(99)00056-5.
Maji, P. K., Biswas R., & Roy, A. R. (2001). Fuzzy Soft Sets. The Journal of Fuzzy Mathematics, 9, 3, 589-602.
Agarwal, M., Biswas, K. K., & Hanmandlu, M. (2013). Generalized intuitionistic fuzzy soft sets with applications in decision-making. Applied Soft Computing, 13(8), 3552-3566. https://doi.org/10.1016/j.asoc.2013.03.015.
Jiang, Y., Tang, Y., Chen, Q., Liu, H., & Tang, J. (2010). Interval-valued intuitionistic fuzzy soft sets and their properties. Computers & Mathematics with Applications, 60(3), 906-918. https://doi.org/10.1016/j.camwa.2010.05.036.
Garg, H., & Arora, R. (2018). Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making. Applied Intelligence, 48, 343-356. https://doi.org/10.1007/s10489-017-0981-5.
Maji, P. K. (2009). More on intuitionistic fuzzy soft sets. In Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Delhi, India, December 15-18, 2009. Proceedings 12 (pp. 231-240). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_28.
Khan, M. J., Kumam, P., Liu, P., Kumam, W., & Ashraf, S. (2019). A novel approach to generalized intuitionistic fuzzy soft sets and its application in decision support system. Mathematics, 7(8), 742. https://doi.org/10.3390/math7080742.
Maji, P. K., Biswas R., & Roy, A. R. (2003). An application of soft sets in a decision-making problem. Computer and Mathematics with Applications, 44, 1077-1083.
Chetia. B., Das. K. P. (2012). Some Results of Intuitionistic Fuzzy Soft Matrix theory. Advances in Applied Science Research, 3(1), 412-423.
Muthukumar, P., & Krishnan, G. S. S. (2016). A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis. Applied Soft Computing, 41, 148-156. https://doi.org/10.1016/j.asoc.2015.12.002.
Çağman, N., & Karataş, S. (2013). Intuitionistic fuzzy soft set theory and its decision making. Journal of Intelligent & Fuzzy Systems, 24(4), 829-836. https://doi.org/10.3233/IFS-2012-0601.
Kharal, A., & Ahmad, B. (2009). Mappings on fuzzy soft classes. Advances in Fuzzy System, 6, 407890. https://doi.org/10.1155/2009/407890.
Jafar, N. M., Muniba, K., Saeed, A., Abbas, S., & Bibi, I. (2019). Application of Sanchez’s Approach to Disease Identification Using Trapezoidal Fuzzy Numbers. International Journal of Latest Engineering Research and Applications, 4(9), 51-57.
Meo, A. S., Zia, I., Bukhari, A. I., & Arain, A. S. (2016). Type-2 diabetes mellitus in Pakistan: Current prevalence and future forecast. Journal of the Pakistan Medical Association, 66(12), 1637-1642.
Jafar, M. N., Saqlain, M., Shafiq, A. R., Khalid, M., Akbar, H., & Naveed, A. (2020). New Technology in Agriculture Using Neutrosophic Soft Matrices with the Help of Score Function. International Journal of Neutrosophic Science, 3(2), 78-88.
Jafar, M.N., Muniba, K., Saeed, A., Abbas, S., & Bibi, I. (2019). Application of Sanchez's Approach to Disease Identification Using Trapezoidal Fuzzy Numbers. International Journal of Latest Engineering Research and Applications, 4(9), 51-57.
Tao, Z., Chen, H., Song, X., Zhou, L., & Liu, J. (2015). Uncertain linguistic fuzzy soft sets and their applications in group decision-making. Applied Soft Computing, 34, 587-605. https://doi.org/10.1016/j.asoc.2015.04.051.
Vijayabalaji, S., & Ramesh, A. (2018). Uncertain multiplicative linguistic soft sets and their application to group decision-making. Journal of Intelligent & Fuzzy Systems, 35(3), 3883–3893. https://doi.org/10.3233/JIFS-18818.
Wang, H., Ju, Y., & Liu, P. (2019). Multi-attribute group decision-making methods based on q-rung orthopair fuzzy linguistic sets. International Journal of Intelligent Systems, 34, 1129- 1157. https://doi.org/10.1002/int.22089.
Wu, X., Liao, H., Benjamin, L., & Zavadskas, E. K. (2023). A Multiple Criteria Decision-Making Method with Heterogeneous Linguistic Expressions. IEEE Transactions on Engineering Management, 70(5), 1857-1870. https://doi.org/10.1109/TEM.2021.3072590.
Saqlain M., Moin, S., Jafar, M. N., Saeed, M., & Smarandache, F. (2020). Aggregate Operators of Neutrosophic Hypersoft Set. Neutrosophic Sets and Systems, 32, 294-306. https://doi.org/10.5281/zenodo.3723155.
Saqlain, M., Sana, M., Jafar, N., Saeed, M., & Said, B. (2020). Single and Multi-valued Neutrosophic Hypersoft set and Tangent Similarity Measure of Single valued Neutrosophic Hypersoft Sets. Neutrosophic Sets and Systems, 32, 317-329. https://doi.org/10.5281/zenodo.3723165.
Saqlain, M., Saeed, M., Zulqarnain, M. R., & Sana, M. (2021). Neutrosophic Hypersoft Matrix Theory: Its Definition, Operators, and Application in Decision-Making of Personnel Selection Problem. Neutrosophic Operational Research, Springer. https://doi.org/10.1007/978-3-03057197-9.
Saqlain M., Riaz, M., Saleem, M. A., & Yang, M. S. (2021). Distance and Similarity Measures for Neutrosophic HyperSoft Set (NHSS) with Construction of NHSSTOPSIS and Applications. IEEE Access, 9, 30803-30816. https://doi.org/10.1109/ACCESS. 2021.3059712.
Jafar, M. N., Saeed, M., Saqlain, M., & Yang, M. S. (2021). Trigonometric similarity measures for neutrosophic hypersoft sets with application to renewable energy source selection. Ieee Access, 9, 129178-129187. https://doi.org/10.1109/ACCESS.2021.3112721.
Jana, C., & Pal, M. (2023). Interval-Valued Picture Fuzzy Uncertain Linguistic Dombi Operators and Their Application in Industrial Fund Selection. Journal of Industrial Intelligence, 1(2), 110-124. https://doi.org/10.56578/jii010204.
Saqlain, M. (2023). Sustainable Hydrogen Production: A Decision-Making Approach Using VIKOR and Intuitionistic Hypersoft Sets. Journal of Intelligent Management Decision, 2(3), 130-138. https://doi.org/10.56578/jimd020303.
Jain, A., Kumar, V., & Kumar, R. (2017). A novel approach for medical decision support system using intuitionistic fuzzy soft matrix. Health Information Science and Systems, 5(1), 1-10.
Majumdar, P., & Samanta, S. K. (2010). Generalized fuzzy soft sets. Computers & Mathematics with Applications, 59(4), 1425–1432. https://doi.org/10.1016/j.camwa.2009.12.006.
Ali, M. I., Feng, F., Liu, X., Min, W. K., & Shabir, M. (2009). On some new operations in soft set theory. Computers & Mathematics with Applications, 57(9), 1547-1553. https://doi.org/10.1016/j.camwa.2008.11.009.
Vijayabalaji, S., & Ramesh, A. (2018). Uncertain multiplicative linguistic soft sets and their application to group decision-making. Journal of Intelligent & Fuzzy Systems, 35(3), 3883-3893. https://doi.org/10.3233/JIFS-18818.
Aiwu Z., & Hongjun G. (2016). Fuzzy-valued linguistic soft set theory and multi-attribute decision-making application. Chaos, Solitons & Fractals, 89, 2-7. https://doi.org/10.1016/j.chaos.2015.09.001.
Tao, Z., Chen, H., Song, X., Zhou, L., & Liu, J. (2015). Uncertain linguistic fuzzy soft sets and their applications in group decision making. Applied Soft Computing, 34, 587-605. https://doi.org/10.1016/j.asoc.2015.04.051.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Spectrum of Engineering and Management Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
