Search Advanced

AIMS Mathematics

2023, Volume 8, Issue 8: 18185-18205. doi: 10.3934/math.2023924
Previous Article Next Article
Research article Special Issues

Medical decision-making techniques based on bipolar soft information

  • 1.
    Department of Mathematics, Quaid-i-Azam University, Islamabad, 45320, Pakistan
  • 2.
    Department of Mathematics, Sana'a University, Sana'a, Yemen
  • 3.
    Department of Mathematics, College of Sciences and Humanities in Aflaj, Prince Sattam bin Abdulaziz University, Riyadh, Saudi Arabia
  • Received: 04 February 2023 Revised: 21 May 2023 Accepted: 22 May 2023 Published: 26 May 2023

  • MSC : 03E72, 90B50

  • Data uncertainty is a barrier in the decision-making (DM) process. The rough set (RS) theory is an effective approach to study the uncertainty in data, while bipolar soft sets (BSSs) can handle the vagueness and uncertainty as well as the bipolarity of the data in a variety of situations. In this article, we introduce the idea of rough bipolar soft sets (RBSSs) and apply them to find the best decision in two different DM problems in medical science. The first problem is about deciding between the risk factors of a disease. Our algorithm facilitates the doctors to investigate which risk factor is becoming the most prominent reason for the increased rate of disease in an area. The second problem is deciding between the different compositions of a medicine for a particular illness having different effects and side effects. We also propose algorithms for both problems.

    Citation: Nosheen Malik, Muhammad Shabir, Tareq M. Al-shami, Rizwan Gul, Abdelwaheb Mhemdi. Medical decision-making techniques based on bipolar soft information[J]. AIMS Mathematics, 2023, 8(8): 18185-18205. doi: 10.3934/math.2023924

    Related Papers:

  • Data uncertainty is a barrier in the decision-making (DM) process. The rough set (RS) theory is an effective approach to study the uncertainty in data, while bipolar soft sets (BSSs) can handle the vagueness and uncertainty as well as the bipolarity of the data in a variety of situations. In this article, we introduce the idea of rough bipolar soft sets (RBSSs) and apply them to find the best decision in two different DM problems in medical science. The first problem is about deciding between the risk factors of a disease. Our algorithm facilitates the doctors to investigate which risk factor is becoming the most prominent reason for the increased rate of disease in an area. The second problem is deciding between the different compositions of a medicine for a particular illness having different effects and side effects. We also propose algorithms for both problems.


    加载中


    [1] T. M. Al-shami, J. C. R. Alcantud, A. Mhemdi, New generalization of fuzzy soft sets: $(a, b)$-Fuzzy soft sets, AIMS Math., 8 (2023), 2995–3025. https://doi.org/10.3934/math.2023155 doi: 10.3934/math.2023155
    [2] J. C. R. Alcantud, The semantics of N-soft sets, their applications, and a coda about three-way decision, Inform. Sci., 606 (2022), 837–852. https://doi.org/10.1016/j.ins.2022.05.084 doi: 10.1016/j.ins.2022.05.084
    [3] M. I. Ali, F. Feng, X. Y. Liu, W. K. Min, M. Shabir, On some new operations in soft set theory, Comput. Math. Appl., 57 (2009), 1547–1553. https://doi.org/10.1016/j.camwa.2008.11.009 doi: 10.1016/j.camwa.2008.11.009
    [4] T. M. Al-shami, Bipolar soft sets: Relations between them and ordinary points and their applications, Complexity, 2021 (2021). https://doi.org/10.1155/2021/6621854 doi: 10.1155/2021/6621854
    [5] T. M. Al-shami, An improvement of rough sets' accuracy measure using containment neighborhoods with a medical application, Inform. Sci., 569 (2021), 110–124. https://doi.org/10.1016/j.ins.2021.04.016 doi: 10.1016/j.ins.2021.04.016
    [6] T. M. Al-shami, Topological approach to generate new rough set models, Complex Intell. Syst., 8 (2022), 4101–4113. https://doi.org/10.1007/s40747-022-00704-x doi: 10.1007/s40747-022-00704-x
    [7] T. M. Al-shami, Improvement of the approximations and accuracy measure of a rough set using somewhere dense sets, Soft Comput., 25 (2021), 14449–14460. https://doi.org/10.1007/s00500-021-06358-0 doi: 10.1007/s00500-021-06358-0
    [8] T. M. Al-shami, D. Ciucci, Subset neighborhood rough sets, Knowl.-Based Syst., 237 (2022), 107868. https://doi.org/10.1016/j.knosys.2021.107868 doi: 10.1016/j.knosys.2021.107868
    [9] T. M. Al-shami, M. E. El-Shafei, $T$-soft equality relation, Turk. J. Math., 44 (2020), 1427–1441. https://doi.org/10.3906/mat-2005-117 doi: 10.3906/mat-2005-117
    [10] T. M. Al-shami, A. Mhemdi, Approximation spaces inspired by subset rough neighborhoods with applications, Demonstr. Math., 56 (2023). https://doi.org/10.1515/dema-2022-0223 doi: 10.1515/dema-2022-0223
    [11] C. G. Aras, T. M. Al-shami, A. Mhemdi, S. Bayramov, Local compactness and paracompactness on bipolar soft topological spaces, J. Intell. Fuzzy Syst., 43 (2022), 6755–6763. https://doi.org/10.3233/JIFS-220834 doi: 10.3233/JIFS-220834
    [12] N. Çağman, S. Enginoglu, Soft matrix theory and its decision making, Comput. Math. Appl., 59 (2010), 3308–3314. https://doi.org/10.1016/j.camwa.2010.03.015 doi: 10.1016/j.camwa.2010.03.015
    [13] N. Çağman, S. Enginoglu, FP-soft set theory and its applications, Ann. Fuzzy Math. Inform., 2 (2011), 219–226.
    [14] D. Chen, E. C. C. Tsang, D. S. Yeung, X. Wang, The parametrization reduction of soft sets and its applications, Comput. Math. Appl., 49 (2005), 757–763. https://doi.org/10.1016/j.camwa.2004.10.036 doi: 10.1016/j.camwa.2004.10.036
    [15] O. Dalkılıç, N. Demirtaş, Decision analysis review on the concept of class for bipolar soft set theory, Comput. Appl. Math., 41 (2022), 205. https://doi.org/10.1007/s40314-022-01922-2 doi: 10.1007/s40314-022-01922-2
    [16] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst., 17 (1990), 191–209. https://doi.org/10.1080/03081079008935107 doi: 10.1080/03081079008935107
    [17] F. Fatimah, D. Rosadi, R. B. Hakim, J. C. R. Alcantud, N-soft sets and their decision making algorithms, Soft Comput., 22 (2018), 3829–3842. https://doi.org/10.1007/s00500-017-2838-6 doi: 10.1007/s00500-017-2838-6
    [18] F. Feng, L. Changxing, B. Davvaz, M. I. Ali, Soft sets combined with fuzzy sets and rough sets: A tentative approach, Soft Comput., 14 (2010), 899–911. https://doi.org/10.1007/s00500-009-0465-6 doi: 10.1007/s00500-009-0465-6
    [19] F. Feng, Y. B. Jun, X. Liu, L. Li, An adjustable approach to fuzzy soft set based decision-making, J. Comput. Appl. Math., 234 (2010), 10–20. https://doi.org/10.1016/j.cam.2009.11.055 doi: 10.1016/j.cam.2009.11.055
    [20] M. K. El-Bably, T. M. Al-shami, Different kinds of generalized rough sets based on neighborhoods with a medical application, Int. J. Biomath., 14 (2021), 2150086. https://doi.org/10.1142/S1793524521500868 doi: 10.1142/S1793524521500868
    [21] M. Hosny, T. M. Al-shami, Rough set models in a more general manner with applications, AIMS Math., 7 (2022), 18971–19017. https://doi.org/10.3934/math.20221044 doi: 10.3934/math.20221044
    [22] R. A. Hosny, B. A. Asaad, A. A. Azzam, T. M. Al-shami, Various topologies generated from $E_j$-neighbourhoods via ideals, Complexity, 2021 (2021). https://doi.org/10.1155/2021/4149368 doi: 10.1155/2021/4149368
    [23] F. Karaaslan, I. Ahmad, A. Ullah, Bipolar soft groups, J. Intell. Fuzzy Syst., 31 (2016), 651–662. https://doi.org/10.3233/IFS-162178 doi: 10.3233/IFS-162178
    [24] F. Karaaslan, S. Karataş, A new approach to bipolar soft sets and its applications, Discret. Math. Algorit., 7 (2015), 1550054. https://doi.org/10.1142/S1793830915500548 doi: 10.1142/S1793830915500548
    [25] X. Ma, Q. Liu, J. Zhan, A survey of decision-making methods based on certain hybrid soft set models, Artif. Intell. Rev., 47 (2017), 507–530. https://doi.org/10.1007/s10462-016-9490-x doi: 10.1007/s10462-016-9490-x
    [26] P. K. Maji, R. Biswas, A. R. Roy, Soft set theory, Comput. Math. Appl., 45 (2003), 555–562. https://doi.org/10.1016/S0898-1221(03)00016-6 doi: 10.1016/S0898-1221(03)00016-6
    [27] N. Malik, M. Shabir, Rough fuzzy bipolar soft sets and applications in decision-making problems, Soft Comput., 23 (2019), 1603–1614. https://doi.org/10.1007/s00500-017-2883-1 doi: 10.1007/s00500-017-2883-1
    [28] T. M. Al-shami, A. Mhemdi, Belong and nonbelong relations on double-Framed soft sets and their applications, J. Math., 2021 (2021). https://doi.org/10.1155/2021/9940301 doi: 10.1155/2021/9940301
    [29] T. M. Al-shami, New soft structure: Infra soft topological spaces, Math. Probl. Eng., 2021 (2021). https://doi.org/10.1155/2021/3361604 doi: 10.1155/2021/3361604
    [30] T. M. Al-shami, Soft somewhat open sets: Soft separation axioms and medical application to nutrition, Comput. Appl. Math., 41 (2022), 216. https://doi.org/10.1007/s40314-022-01919-x doi: 10.1007/s40314-022-01919-x
    [31] T. M. Al-shami, M. E. El-Shafei, On supra soft topological ordered spaces, Arab J. Basic Appl. Sci., 26 (2019) 433–445. https://doi.org/10.1080/25765299.2019.1664101 doi: 10.1080/25765299.2019.1664101
    [32] L. D. R. Kočinac, T. M. Al-shami, V. Çetkin, Selection principles in the context of soft sets: Menger spaces, Soft Comput., 25 (2021), 12693–12702. https://doi.org/10.1007/s00500-021-06069-6 doi: 10.1007/s00500-021-06069-6
    [33] D. Molodtsov, Soft set theory—first results, Comput. Math. Appl., 37 (1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
    [34] M. Naz, M. Shabir, On fuzzy bipolar soft sets, their algebraic structures and applications, J. Intell. Fuzzy Syst., 26 (2014), 1645–1656. https://doi.org/10.3233/IFS-130844 doi: 10.3233/IFS-130844
    [35] T. Y. Ozturk, On bipolar soft topological spaces, J. New Theor., 20 (2018), 64–75.
    [36] Z. Pawlak, Rough sets, Int. J. Inf. Comput. Sci., 11 (1982), 341–356. https://doi.org/10.1007/BF01001956 doi: 10.1007/BF01001956
    [37] Z. Pawlak, Rough sets: Theoretical aspects of reasoning about data, Kluwer, Dordrecht, 1991.
    [38] Z. Pawlak, A. Skowron, Rudiments of rough sets, Inf. Sci., 177 (2007), 3–27. https://doi.org/10.1016/j.ins.2006.06.003 doi: 10.1016/j.ins.2006.06.003
    [39] M. Shabir, A. Bakhtawar, Bipolar soft connected, bipolar soft disconnected and bipolar soft compact spaces, Songklanakarin J. Sci. Technol., 93 (2017), 359–371.
    [40] M. Shabir, M. Naz, On bipolar soft sets, arXiv: 1303.1344, 2013.
    [41] T. Mahmood, A novel approach towards bipolar soft sets and their applications, J. Math., 2020 (2020). https://doi.org/10.1155/2020/4690808 doi: 10.1155/2020/4690808
    [42] J. Sanabria, K. Rojo, F. Abad, A new approach of soft rough sets and a medical application for the diagnosis of coronavirus disease, AIMS Math., 8 (2023), 2686–2707. https://doi.org/10.3934/math.2023141 doi: 10.3934/math.2023141
    [43] Y. Zou, Z. Xiao, Data analysis approaches of soft sets under incomplete information, Knowl.-Based Syst., 21 (2008), 941–945. https://doi.org/10.1016/j.knosys.2008.04.004 doi: 10.1016/j.knosys.2008.04.004
    [44] J. Zhan, K. Zhu, Z-soft rough fuzzy ideals of hemirings and corresponding decision-making, Soft Comput., 21 (2017), 1923–1936. https://doi.org/10.1007/s00500-016-2119-9 doi: 10.1007/s00500-016-2119-9
    [45] J. Zhan, M. I. Ali, N. Mehmood, Z-soft fuzzy rough set model and corresponding decision-making methods, Appl. Soft Comput., 56 (2017), 446–457. https://doi.org/10.1016/j.asoc.2017.03.038 doi: 10.1016/j.asoc.2017.03.038
  • Reader Comments
  • © 2023 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)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
AIMS Mathematics
1.8 3.4

Metrics

Article views(767) PDF downloads(76) Cited by(0)

Preview PDF
Download XML
Export Citation
Article outline

Figures and Tables

Tables(10)

Other Articles By Authors

Related pages

Tools

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog