Abstract
<abstract><p>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.</p></abstract>
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