A Hybrid Particle Swarm and Genetic Algorithm Approach for Optimizing Recurrent and Radial Basis Function Networks in Cloud Computing for Healthcare Disease Detection

Abstract

Background: Cloud computing (CC), artificial intelligence (AI), and the Internet of Things (IoT) are combining to revolutionise healthcare by making it possible to process data in real time and diagnose diseases. For increased accuracy and efficiency, optimised solutions are required because traditional approaches are unable to handle the volume and complexity of data from IoT-enabled devices.
Objective: The objective of this research is to develop and apply a hybrid model that combines Radial Basis Function Networks (RBFN), Genetic Algorithms (GA), and Particle Swarm Optimisation (PSO) to improve accuracy, shorten processing times, and improve real-time healthcare disease diagnosis for chronic sickness identification.
Methods: Optimisation of Recurrent Neural Networks (RNNs) and RBFNs for processing real-time data gathered from IoT devices was done using a hybrid PSO-GA framework. Preprocessing of the data occurred with cloud computing for extensive analysis, and optimisation were done to improve sensitivity and specificity metrics for various medical applications.
Results: The suggested PSO-GA RNN-RBFN model performed exceptionally well, with 94% specificity, 92% sensitivity, and 93% accuracy. It reduced processing time to 54 seconds and outperformed conventional techniques like CNN, DeepDR, and EKF-SVM, making it ideal for real-time healthcare monitoring.
Conclusion: A reliable method for real-time illness detection is provided by the combination of PSO, GA, and RBFN in a cloud-based architecture. As compared to conventional methods, the hybrid model dramatically increases accuracy, sensitivity, and specificity, proving its usefulness in managing intricate, urgent healthcare data.