Exploring Blockchain-Based Trust Models in IoT-Driven Healthcare Systems: A Machine Learning Approach

Abstract

The IoT devices are experiencing growth in the healthcare industry as they are being used to track the health status of patients, to treat them and also to perform other functions. However, the IoT-based healthcare system has enormous problems regarding data security, confidentiality, and trust, especially when salient medical information is being transferred across various networks. One of the potential answers to these hiccups is the blockchain technology that is not centralized, immutable and transparent. Together with that, trust models could be enhanced by utilising the capabilities of Machine Learning (ML), through predictive analytics, anomaly detection, and real-time decision-making, which will further increase the security and efficiency of the IoT healthcare system. The article explains the process of the integration of such tools as Blockchain-based trust models, and Machine Learning used in IoT-powered healthcare environments. This research paper aims at developing an efficient solution on how to protect the IoT health networks, open data sharing, and develop confidence between the devices and the healthcare provider and, patients. Using the case studies and simulations, we represent how Blockchain and ML can be applied jointly so that to develop safe, effective, and scalable health applications. The paper also disadvantages and provides a glimpse of what will be done on the future research of using the technologies to enhance the delivery of the healthcare.