Cloud-centric IoT based disease diagnosis healthcare framework

In the last few years, the m-healthcare applications based on Internet of Things (IoT) have provided multi-dimensional features and real-time services. These applications provide a platform to millions of people to get health updates regularly for a healthier lifestyle. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. The big data generated by IoT devices in healthcare domain is analyzed on the cloud instead of solely relying on limited storage and computation resources of handheld devices. Relative to this context, a cloud-centric IoT basedm-healthcare monitoring disease diagnosing framework is proposed which predicts the potential disease with its level of severity. Key terminologies are defined to generate user-oriented health measurements by exploring the concept of computational sciences. The architectural prototype for smart student healthcare is designed for application scenario. The results are computed after processing the health measurements in a specific context. In our case study, systematic student perspective health data is generated using UCI dataset and medical sensors to predict the student with different disease severity. Diagnosis schemes are applied using various state-of-the-art classification algorithms and the results are computed based on accuracy, sensitivity, specificity, and F-measure. Experimental results show that the proposed methodology outperforms the baseline methods for disease prediction.