Effectiveness of Machine and Deep Learning in IOT-Enabled Devices for Healthcare System

The healthcare industry has been one of the fastest to accept the Internet of Things; the main reason for this development is that assimilating IoT features into medical devices significantly improves the superiority and efficiency of the service, carrying a particularly high value for the aged patients in prolonged situations and those demanding continuous observations. Nowadays, a number of IoT-enabled medical devices exist in the market for assistance and correct measurement of information related to our health, such as remote temperature checking for vaccinations, medical data transmitting methods, air quality sensors, drug efficiency tracking, vital science data sizing, sleep monitor systems, medicine refill reminder technologies, and remote care biometric scanners for sleep and safety tools for children. The first section of the chapter consisted of the usage and development of IoT devices in the healthcare system for better care of patients. The second section of the chapter is also comprised of the framework of IoT for healthcare, role and importance of IoT-enabled device in healthcare, and operation of IoT in healthcare components and Internet of Medical Things. The third section of the chapter contained a reported work which covered the IoT along with its different learning models, including machine and deep learning in different healthcare components. The main section of the chapter covered the comparative analysis based on the types of disease detection, datasets used with learning techniques, and performance parameters.