Smart Medical Device Selection Based on Interval Valued Intuitionistic Fuzzy VIKOR
Advances in wireless communication technologies and the internet of things are leading to new developments in the domain of wearable, smart medical devices (SMDs) as a major disruptive trend for the medical industry. Wearable smart sensor technology with non-invasive or invasive implantable materials has a great potential for interfacing with the human body, thanks to low-power silicon-based electronics that are very efficient in data processing and transmission. Novel SMDs are designed for monitoring living being’s vital signs, such as blood pressure, cardiac monitoring, respiration rate, body temperature, etc. in either medical diagnostic or health monitoring. Considering various smart devices in the medical industry, a key decision is which device to choose and apply on the patient. The decision on the evaluation of SMDs is a complicated problem that needs to be assessed from different perspectives. This study guides decision makers on the selection of SMDs of wearable vital sign sensors under different evaluation criteria. A multi criteria decision making approach is proposed to support the SMD selection process under group decision making (GDM) in an uncertain environment. A significant feature of this analysis is the complexity of the selected decision criteria for the SMD evaluation. To simulate these processes, a methodology that combines interval valued intuitionistic fuzzy (IVIF) with Višekriterijumsko kompromisno rangiranje (VIKOR) under GDM is proposed. This methodology is then used to measure the assessment of four SMDs using five evaluation criteria. To validate the proposed approach, the selection methodology for wearable vital sign monitoring devices is applied on a case study.
KeywordsSmart medical devices (SMDs) Multi criteria decision making (MCDM) Group decision making (GDM) Interval valued intuitionistic fuzzy (IVIF) Višekriterijumsko kompromisno rangiranje (VIKOR)
The authors are grateful to the industrial experts who shared their evaluations for the case study. This research is financially supported by Galatasaray University Research Fund (Projects No: 16.402.004 and 17.402.009).
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