Edge Computing with Fog-cloud for Heart Data Processing using Particle Swarm Optimized Deep Learning Technique

Abstract

Chronic illnesses such as heart disease, diabetes, cancer, and respiratory diseases are complex and pose a significant threat to global health. Processing heart data is particularly challenging due to the variability of symptoms. However, advancements in smart wearable devices, computing technologies, and IoT solutions have made heart data processing easier. This proposed model integrates Edge-Fog-Cloud computing to provide rapid and accurate results, making it a promising solution for heart data processing. Patient data is collected using hardware components, and cardiac feature extraction is used to obtain crucial features from data signals. The Optimized Cascaded Convolution Neural Network (CCNN) processes these features, and the CCNN's hyperparameters are optimized using both PSO (Particle Swarm Optimization) and GSO(Galactic Swarm Optimization) techniques. The proposed system leverages the strengths of both optimization algorithms to improve the accuracy and efficiency of the heart data processing system. The GSO-CCNN optimizes the CCNN's hyperparameters, while the PSO-CCNN optimizes the feature selection process. Combining both algorithms enhances the system's ability to identify relevant features and optimize the CCNN's architecture. Performance analysis demonstrates that the proposed technique, which integrates Edge-Fog-Cloud computing with combined PSO-CCNN and GSO-CCNN techniques, outperforms traditional models such as PSO-CCNN, GSO-CCNN, WOA-CCNN, and DHOA-CCNN, which utilize traditional cloud and edge technologies. The proposed model is evaluated in terms of time, energy consumption, bandwidth, and the standard performance metrics of accuracy, precision, recall, specificity, and F1-score. Therefore, the proposed system's comparative analysis ensures its efficiency over conventional models for heart data processing.