Machine Learning For Iot Health Care Applications

Abstract

The Internet of Things (IoT) and machine learning (ML) have broad applications in many facets of life, including healthcare. The traditional approaches to patient services reduced as a result of the internet's quick expansion and improvement, and were replaced by electronic healthcare systems. The most cutting-edge environment for medical devices is provided to patients and medical professionals via the usage of IoT technology. Machine learning and IoT devices are useful in a variety of categories, from remote monitoring of the contemporary climate to mechanical mechanisation. Additionally, medical care applications are mostly exhibiting interest in IoT objects due to cost reduction, simplicity of understanding, and improvements in patient satisfaction. For intelligent, original solutions, the most recent applications for IoT medical care that have been researched and are currently having issues in the clinical setting are required. For calculating the data transfer, specialised, portable, and implantable IoT model devices were examined. Implantable technologies enable the natural replacement of the damaged human body component. The problems that were encountered in developing a wearable and implanted healthcare body area network are highlighted in this research. In this paper, an overview of IoT and machine learning based on healthcare care is presented in detail. The applications that use Machine Learning (ML) for the Internet of Things (IoT) for health care are listed along with all problems and difficulties encountered when using these applications or devices for health care, as well as their significant applications. Additionally, by displaying earlier work and categorising each item according to the technique employed, the methods utilised by machine learning in IoT for creating devices are shown.