Evaluation of smart health technologies with hesitant fuzzy MCDM methods

Abstract

Smart health is applied in many cities around the world as an approach that has a significant potential to solve the problems with smart technologies. Smart health technologies provide effective healthcare services such as personalization of treatments through big data, robotics in cure and care, artificial intelligence support to doctors, etc. The mixed structure of the evaluation of smart health technologies involves many various and contradictory criteria. However, when information is in an uncertain nature, it is difficult to decide on and evaluate. Therefore, hesitant fuzzy linguistic term set (HFLTS) approach is applied to overcome the uncertainty of this multi-criteria decision-making (MCDM) problem. This approach can be used to facilitate experts’ decision-making processes in complex and uncertain situations. In this study, integrated hesitant fuzzy linguistic (HFL) MCDM approach is proposed to evaluate smart health technologies. The criteria are weighted with HFL Analytic Hierarchy Process (AHP), and then, smart health technologies are evaluated with the HFL Combinative Distance-based Assessment (CODAS) method. Lastly, the potential of this approach is presented through a case study.