Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing frameworks have various flaws, such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network community. In this proposed work, Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart disease. Initially, the data is collected, and then pre-processing is done by Linear Discriminant Analysis. Then the features are extracted and optimized using Galactic Swarm Optimization. The optimized features are given into the Health Fog framework for diagnosing heart disease patients. It uses ensemble-based deep learning in edge computing devices, which automatically monitors real-life health networks such as heart disease analysis. Finally, the classifiers such as bagging, boosting, XGBoost, Multi-Layer Perceptron (MLP), and Partitions (PART) are used for classifying the data. Then the majority voting classifier predicts the result. This work uses FogBus architecture and evaluates the execution of power usage, bandwidth of the network, latency, execution time, and accuracy.