Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction

Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the upcoming years to improve the assistance systems. The IoT devices utilize several sensor devices that able to collect a large volume of data in different domains which is processed by AI techniques to make the decision about the assistance problems. Among several applications, in this work, IoT with AI is used to examine the healthcare sectors to improve patient assistance and patient care in the future direction. Traditional health care assistance system fails to predict the exact patient health information and needs which reduces the accuracy of patient assistance process. For these issues, an IoT sensor with AI is used to predict the exact patient details such as fitness tracker, medical reports, health activity, body mass, temperature, and other health care information which helps to choose the right assistance process. Healthcare mobile application is used to achieve this goal and collect the patient's information. This information is shared in the cloud environment, which is accessed and processed by applying the optimized machine learning techniques. The gathered patient details are processed according to the iterative golden section optimized deep belief neural network (IGDBN). The introduced network examines the patient's details from the previous health information which helps to predict the exact patient health condition in the future direction. The efficiency of IoT sensor with an AI-based health assistance prediction process is developed using MATLAB tool. Excellence is determined in terms of precision (99.87), loss error (0.045), simple matching coefficient (99.71%), Matthews correlation coefficient (99.10%) and accuracy (99.86%). (C) 2020 Elsevier Ltd. All rights reserved.