INTEGRATION OF MACHINE LEARNING WITH BLOCKCHAIN FOR HEALTHCARE SYSTEM

Abstract

The developments that took place in the fields of Blockchain Technology, Internet of Things (IoT), and Machine Learning (ML) offer promising and exciting developments within the paradigm shift that took place within various sectors, especially within the health industry. The intersection of these three will address issues on data security and privacy within the context of real-time decision-making. Within this research study, the intersection of IoMT and Blockchain technologies will be explored within the context of how the implementation of Blockchain Technology supports a secure data transfer process within a decentralized IoT environment. Second is the Federated Learning (FL) within the context of ML and its role within the privacy of ML. We analyze 16 recent papers that combine the use of Blockchain, IoT, and ML models, especially for applica- tions in the areas relating to healthcare, security, and others associated with 6G communication technologies. The papers show that the application of Blockchain technology enhances the management of healthcare data from the IoT, while ML models using healthcare datasets from the IoT improve real- time healthcare analysis and anomaly identification.Moreover, the combination of FL with Blockchain technology provides a secure framework for collaborative learning among devices using IoT technology. However, despite the vast potential benefits, there are also challenges in the realm of scalability, computational complexity, privacy concerns in relation to the use of data, and a lack of legal framework regulation that currently hinder the broader adoption of these combined platforms. This article will offer a broad review on the present status of related research experiments and advance future directions related to the ability of 6G communications to help provide a seamless combination of these concepts for the development of intelligent, safe, and optimized IoT platforms.