Assessment of Big Data Vendors by Intuitionistic Fuzzy TODIM

Abstract

Novel technologies can reduce the overall cost in management and/or prevention of illnesses. Since the increased capability of access to high-speed internet and use of smartphones, the use of software or applications (apps) to manage health needs is continuously increasing among patients. The smart devices using these technologies can monitor and track the health of patients in real time. The generated data from these devices are mostly wasted or remains vastly under-utilized. The recent rise in predictive analytics and big data sets to augment the growth of smart medical devices. This brings the question of how to find the best big data vendor for smart medical devices industry. Multi-Criteria Decision-Making (MCDM) is one of the tools, which might be used to assist decision makers (DMs) to assess big data vendors for smart medical devices under different evaluation criteria. Hence, this paper explores how to find the best big data vendor by an MCDM approach, namely TODIM (Portuguese: Tomada de Decisao Interativa Multicriterio). This paper also uses the Intuitionistic Fuzzy objective world environment, which is characterized by the degree of membership, non-membership, and hesitancy to describe uncertain information more comprehensively. A case study based on smart medical devices is used to identify the best alternative. The paper is concluded by providing the criteria set for big data vendor selection and research areas for future research.