Quantifying gesture information in brain hemorrhage patients using probabilistic dual hesitant fuzzy sets with unknown probability information

Uncertainty occurs an a relevant amount in the medical problems where the patient cannot communicate his symptoms to the doctor. This often occurs in intense hemorrhage cases. In such situations, there might be over one membership (non-membership) values, and they might have different occurrence probabilities associated with them. Because of these hesitant values and their probabilities, there are huge chances for the loss of information during addressing sensitive situations. To avoid such valuable information in the medical diagnosis of hemorrhage strokes, probabilistic dual hesitant fuzzy sets (PDHFSs) can be used which embeds the features of dual hesitant fuzzy sets (DHFSs) with the probabilistic information to represent the imprecise available information. Meanwhile, a Maclaurin symmetric mean (MSM) has a prominent characteristic and striking ability to capture inter-relationship among the multi-input arguments. So making full utilization of the features possessed by PDHFSs and MSM, in this paper, we propose some weighted averaging and geometric MSM aggregation operators to address the uncertainties in the medical diagnosis problems and handle the gesture quantification. Some desirable properties of the operators are discussed and an optimization model for determining the probabilities in PDHFSs is framed making use of Shanon's entropy. A decision-making (DM) approach is framed based on the proposed operators followed by comparison analysis and a case study on gesture quantification of a patient suffering from hemorrhage strokes is conducted. The validity of the proposed method is examined with several predominating studies.