Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review
This is an incremental review that synthesizes existing knowledge for researchers in healthcare and IoT, without introducing new methods.
The paper reviews machine learning applications in healthcare IoT, addressing challenges like device diversity and decentralized data, and highlights open issues for future research.
In recent years, smart healthcare IoT devices have become ubiquitous, but they work in isolated networks due to their policy. Having these devices connected in a network enables us to perform medical distributed data analysis. However, the presence of diverse IoT devices in terms of technology, structure, and network policy, makes it a challenging issue while applying traditional centralized learning algorithms on decentralized data collected from the IoT devices. In this study, we present an extensive review of the state-of-the-art machine learning applications particularly in healthcare, challenging issues in IoT, and corresponding promising solutions. Finally, we highlight some open-ended issues of IoT in healthcare that leaves further research studies and investigation for scientists.