IOT enabled hybrid model with learning ability for E-health care systems

Abstract

One of the most cutting-edge technologies over the years is the Internet of Things (IoT), which is a major force behind the paradigm shift away from conventional medical practises. The goal of IoT-based eHealth is to provide healthcare services that are more effective and individualised through continuous data exchange between linked devices and enhanced data analytics. The IoT and decision-making systems are the main areas of focus of this programme, which seeks to deliver intelligent and proactive healthcare. By considering the huge array of physiological characteristics and applying potent analytical tools like cluster analysis, it is possible to obtain more insight into health-data. In this study, e-health technologies and remote patient monitoring were developed to assist patients in avoiding hospital visits, especially during viral epidemics. This project will use IoT and artificial intelligence (AI) technology to address these problems. The study's objective is to select the most appropriate and effective number of hidden layers and activation function types for the deep net (NN). Describe the patient data sent using IoT protocols next. NN analyses the information from the patient's medical sensors to choose the optimal option. The diagnosis is then communicated to the physician. The proposed technology enables patients to autonomously recognise and forecast the sickness while also supporting clinicians in remote disease discovery and analysis without requiring patients to attend the hospital.