Machine Learning-Based Security Solutions for Healthcare: An Overview

ABSTRACT

The eruption of chronic diseases such as COVID-19 has re-emphasized the need for people all over the world to have access to urgent healthcare. The latest pandemic has shown the flaws in the conventional healthcare system, namely that hospitals and clinics alone are incapable of dealing with such a crisis. Smart and integrated wearables are one of the main technologies that favor modern healthcare solutions (Godi, B., Viswanadham, S., Muttipati, A.S., Prakash Samantray, O., Gadiraju student, S.R. E-Healthcare Monitoring System using IoT with Machine Learning Approaches. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). pp. 1–5. IEEE, Gunupur, India (2020). https//doi.org/10.1109/ICCSEA49143.2020.9132937 ). These wearables can now gather data on an unparalleled scale thanks to advancements in the Internet of Things (IoT). Healthcare is among many fields that have been transformed by IoT, with the Internet of Medical Things (IoMT) systems being introduced as an IoT branch. Patients with chronic diseases can be monitored remotely using IoMT systems. As a result, it can offer prompt diagnostics to patients, potentially saving their lives in the event of an emergency. However, protection in these vital systems is a major issue that has arisen as a result of their widespread use. This paper presents an overview of the technologies that are being used on IoMT as well as some security problems found in the literature. This survey will give an insight to the readers regarding the importance of security in healthcare and the different machine learning methods used to address that issue. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.