Study of Machine and Deep Learning Classifications for IOT Enabled Healthcare Devices

Abstract

The Internet of Things (IoT) is bringing a new revolution in academia and research. It has penetrating roots, which are bringing remarkable changes in various domains, especially healthcare. The advancements in other technologies like wearable devices, sensors, cloud-based computing have led to its proliferation. IoT has led the transition from traditional center-based systems to personalized healthcare systems (PHS). It is no wonder that such advanced and robust technology has its associated challenges and leggings like increased cost, the increased storage requirement for data storage, maintenance of heterogeneity of operable devices, and many more. This presented work deals with studying such a robust technique, IoT, and its applications in the healthcare domain along with machine learning and deep learning techniques. It describes the framework of an IoT-enabled system, its benefits, and present applications. This article also apprises its challenges and, most importantly, the study of various researchers to design IoT-enabled healthcare systems using various machine learning and deep learning algorithms. The study reveals that IoT is successful in establishing better relationships between healthcare professionals and patients, diagnosing the forthcoming medically critical conditions, and helps manage the medical resources effectively.