Cloud-centric IoT based student healthcare monitoring framework

Among the extensive and impressive collection of applications enabled by IoT, smart and interactive healthcare is a particularly important one. To gather rich information indicator of our mental and physical health, IoT based sensors are either worn on the body or embedded in the living environment. Moreover, by incorporating the mobile computing technology in IoT based healthcare systems, the reactive care system can be transformed to proactive and preventive healthcare systems. Relative to this context, a cloud-centric IoT based smart student m-healthcare monitoring framework is proposed. This framework computes the student diseases severity by predicting the potential disease with its level by temporally mining the health measurements collected from medical and other IoT devices. To effectively analyze the student healthcare data, an architectural model for smart student health care system has been designed. In our case study, health dataset of 182 suspected students are simulated to generate relevant waterborne diseses cases. This data is further analyzed to validate our model by using k-cross validation approach. Pattern based diagnosis scheme is applied using various classification algorithms and then results are computed based on accuracy, sensitivity, specificity and response time. Experimental results show that Decision tree (C4.5) and k-neighest neighbour algorithms perform better as compared to other classifiers in terms of above mentioned parameters. Moreover, the proposed methodology is effective in decision making by delivering time sensitive information to caretaker or doctor within specific time. Lastly, the temporal granule pattern based presentation reterives effective diagnosis results for the proposed system.