ABSTRACT
Currently, Internet of Health Things (IoHT)–based e-healthcare applications and services are found to be promising in several ways. IoHT is a distributed network comprised of smart Internet of Things (IoT) devices, machines, and models that have the ability to interact with one another. By the use of IoT gadgets and advanced technologies in the healthcare sector, a massive quantity of data has been generated and is stored in the cloud. Here, IoT and cloud-based applications are found useful in distinct healthcare applications. To avail prominent e-healthcare services to clients, this chapter presents an IoT and cloud-based disease diagnosis model. A particle swarm optimization (PSO)–based artificial neural network (ANN) called the PSO-ANN model is presented to monitor the diagnosis of the presence of diabetes and its severity level. The application of the PSO algorithm helps to optimize the weight of the ANN model. The data from the benchmark data set and IoT gadgets are used for validation. The validation of the presented PSO-ANN model has been tested using a benchmark diabetes data set. The outcome offered from the experimental analysis clearly pointed out the superior characteristics of the PSO-ANN model over compared methods.
