Machine and Deep Learning Classifications for IoT-Enabled Healthcare Devices

The emergence of the Internet of Things (IoT) is profoundly influencing academic research and will have longterm consequences on several industries, chief among them the healthcare sector. The healthcare industry has changed as a result of the Internet of Things (IoT), which has replaced old, centralized processes with decentralized networks of smart devices. Systems of customized healthcare have been developed as a result of this (PHS). This change has been made possible by recent advancements in fields like wearables, sensor networks, and cloud computing, which have simplified the use of IoT in healthcare settings on a large scale. Notwithstanding a few significant disadvantages, IoT has the potential to significantly transform the medical industry. Keeping up with a large number of devices, rising costs, and the requirement for more data storage space are the challenges. This paper offers a thorough examination of IoT as a flexible and robust technology, emphasizing its applications in the healthcare industry. Research is being done on the interaction between machine learning and deep learning. In addition to examining the various advantages and real-world applications of the technology, this article covers the architecture of an Internet of Things (IoT)-enabled healthcare system. It draws attention to the challenges posed by the IoT healthcare environment and emphasizes the need for academics to come up with novel solutions to these problems. The present work of several researchers in the fields of machine learning and deep learning, which are extensively used in IoT-enabled healthcare systems, is highlighted in this study.