A Novel IoT-Fog-Cloud-based Healthcare System for Monitoring and Preventing Encephalitis

In 2019, the outbreak of Acute Encephalitis Syndrome (AES) outbreak occurred in the Bihar region of India. AES, a viral infection that affects the immune system of the human, is recognized as public health concern globally. The objective of this study is to monitor and prevent the spread of Encephalitis (ENCPH). Spatio-temporal-based Temporal-Recurrent Neural Network (T-RNN) prediction model is used to control the outbreak and generate an alarming signal to the medical caregiver in case of abnormality. T-RNN model is appended with novel Self-Organized Mapping (SOM) technique for outbreak visualization geographically. The current work presents a Tri-logical IoT-fog-cloud (TIFC) model to collect AES data for monitoring, and controlling the outbreak over the Spatio-temporal manner. Different events are correlated over the Spatio-temporal patterns in the form of a time-series granule at a different timestamps. Fuzzy C-Means (FCM) classifier is used to analyze the category of a patient based on health-related data parameters. Henceforth, for effective health-oriented decision-making and information deliverance to the user, a prediction model based on Spatio-temporal is used to manage the medical resources. For validation purposes, numerous simulations have been performed over real-data sets, and the results are compared with different state-of-the-art prediction models. Based on simulations, it can be concluded that the proposed system has outperformed other decision models in terms of statistical parameters including accuracy, f-measure, and reliability. Future research needs to focus on the security aspect for prevention and control for infectious viruses.