Student Health Detection using a Machine Learning Approach and IoT

With the development of sensor technologies, Internet of Things (IoT) technologies for creating behavioral and physiological management systems, including IoT-based pupil medical management systems, have evolved quickly. It is now required to monitor the health function status of the increasing number of students who live alone and are dispersed across large geographic areas. An IoT based methodology of management student heath is put forth in this study to continually monitor students’ vital signs and identifies biological and behavioral changes using advanced medical technology. In this concept, critical data are gathered by the IoT module, & data evaluation is done using neural network models are used to analyze the data to determine the likely risks to kids' physiological and behavioral modifications. According to the experimental findings, the suggested model is effective and accurate in determining the state of the pupils. The support vector machine had a maximum performance of 99.1% after the suggested model had been evaluated, which is a good outcome for our objectives. Algorithms for multilevel perceptron neural systems, random forests & decision trees were also beaten by the results.