Cloud Computing with Artificial Intelligence Techniques: GWO-DBN Hybrid Algorithms for Enhanced Disease Prediction in Healthcare Systems

Abstract

Background: Cloud computing, AI, and IoT technologies are revolutionizing healthcare by enabling predictive analytics and ongoing health monitoring. A hybrid technique that combines the Gray Wolf Optimization (GWO) algorithm with Deep Belief Networks (DBN) enhances disease prediction and real-time patient monitoring, particularly for chronic disorders. Objective: Designing andimplementing a GWO-DBN hybrid model that improves predictiveaccuracy for chronic disease monitoring in cloud environments is the goal of this project. Utilizing wearable IoT devices and cloud capabilities, the goal is to combine scalable, real-time analysis for healthcare providers for reliable, remote illness management. Methods: Data collection, preprocessing, cloud storage, optimization, and monitoring are all integrated into the hybrid GWO-DBN architecture. The GWO algorithm chooses important features and optimizes DBN parameters. Cloud infrastructure enables real-time alerts, enabling prompt responses by healthcare providers based on ongoing patient health parameters. Results: The GWO-DBN model outperforms conventional methods with a 93% prediction
accuracy, 90% sensitivity, and 95% specificity. A useful, high-performing solution for disease monitoring and resource optimization in the healthcare industry is offered by the cloud integration, that guarantees scalability and real-time notifications for healthcare providers. Conclusion: In real-time disease monitoring applications, the GWO-DBN model exhibits enhanced operational scalability, predictive accuracy, and efficiency. This cloud-based hybrid solution provides a viable model for proactive healthcare by enabling early diagnosis andresource allocation.