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AIMS Mathematics

2023, Volume 8, Issue 8: 18185-18205. doi: 10.3934/math.2023924
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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.


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