Performance Interpretation of Machine Learning Based Classifiers for e-HealthCare System in Fog Computing Network

Internet of Things (IoT) devices generate a lot of data periodically in the internet era. In order to process that data in real-time, high storage capacity and computational power is required to overcome the limitation of the low processing capabilitiesand storage ability of these devices. Cloud computing solution offers large storage capacity and powerful computational facility but increases the response time due to network latency. To address the issue, the idea of Fog computing was introduced by bringing the computational services closer to the peripheral device of the network. The internet-enabled e-healthcaresystem allows doctors to conduct remote patient monitoring and demands real-time decision-making for critical data. Smart decision-making for classifying the data gathered into high and low-risk data has been considered in some of the existing research. In this context, we designed a framework to analyzethe accuracy of e-healthcare systems by employing machine learning approaches for data classification in fog computing. Also, comparative analysis has been done, highlighting the performance of data classification approaches. Simulation resultsshow that K-Nearest Neighbor and Support Vector Machine classifiers perform better than other classifiers observed and fog performance is significantly high in comparison with cloud.