IoT and Fog Computing Based Prediction and Monitoring System for Stroke Disease

Abstract

In recent times, stroke is become a considerably health issue for most people throughout the world due to bad dietary habit including smoke and drink, working pressure, lack of physical activity etc. Sometimes, the condition of patients become worst due to lack of information as medical staff are not aware about the physiological attribute of stroke and recovery time will be increased. Nowadays, healthcare domain gets wide attention among research community due to incremental data growth, advanced diagnostic tools, medical imaging process and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is quit tough task due to its nature. Large number of machines learning techniques are presented for handling the healthcare data and right diagnosis of disease. On the other side, IoT, cloud and fog computing are trending research areas for disease diagnosis and prediction and get wide attention from research community. Several healthcare systems have been developed using IoT, Cloud and Fog computing-based technologies. Hence, in this work, an IoT and fog computing-based monitoring system is developed for diagnosis and prediction of stroke. Further, an ensemble classifier is integrated in proposed fog computing-based monitoring system to predict and monitor the stroke infection.  The efficiency of proposed monitoring system is tested over stroke disease dataset. This dataset is collected from various hospital located in Delhi-NCR region during 2016-2018. The simulation results of proposed monitoring system are evaluated using accuracy, sensitivity and specificity parameters and provides state of art results.