Synergy of Machine Learning and IoT for Healthcare: Enhancing Sustainable Societies in Uncertain Environments

Machine learning (ML) and Internet of Things (IoT) play a vital role in various disciplines including, computational intelligence, artificial intelligence, healthcare systems and neural network, etc. In this study, the synergy of ML and IoT is successfully used for the creation of high-performance control components of healthcare systems, where comprehensive health data can be used for research and improving healthcare services. By linking medical networks, equipment, and facilities to the internet, the IoT is revolutionizing the healthcare sector, facilitating easy data interchange and improving patient care. IoT in healthcare primarily enhances patient outcomes through medical problem detection and monitoring. Many type of medical equipment, including glucose meters, are intended to capture patient information and symptoms. In the context of multi-criteria decision-making (MCDM), the criteria importance through intercriteria correlation (CRITIC) and combined compromise solution (CoCoSo) approaches have shown to be effective in calculating the weights of criteria and ranking of alternatives, respectively. Our main goal in this study is to introduce a novel approach for MCDM based on control parameters of linear Diophantine fuzzy sets (LDFSs). The suggested approach is practically applied for the selection of an efficient IoT healthcare system. In order to elucidate the validity, reliability, and efficiency in the patient care, a sensitivity analysis and comparative analysis are also carried out.