Abstract
In the realm of healthcare, the continuous evolution of monitoring systems demands innovative solutions to ensure heightened reliability and accuracy. This paper introduces a pioneering approach to healthcare monitoring through a hybrid deep learning model that combines the advantages of recurrent neural networks (RNN) and deep neural networks (DNN). Focused on enhancing connectivity in Software Defined Networking (SDN), our framework places a significant emphasis on anomaly detection for improved predictive accuracy. The proposed Hybrid Deep Learning model is meticulously designed to harness the complementary features of DNN and RNN, enabling the system to capture both spatial and temporal dependencies in healthcare data. This integration enhances the precision of anomaly detection, allowing for the identification of subtle deviations from normal patterns with unprecedented accuracy. Key to our methodology is the adaptability of Software Defined Networking, providing a flexible and programmable infrastructure. The Hybrid Deep Learning model operates seamlessly within this SDN framework, dynamically optimizing resource allocation and traffic patterns to accommodate the unique demands of healthcare monitoring. Through extensive experimentation and validation, our framework demonstrates remarkable predictive accuracy in identifying anomalies within healthcare data streams. Comparative analyses against traditional anomaly detection methods underscore the superiority of our approach, showcasing its efficacy in real-world healthcare scenarios. In conclusion, our research contributes to the advancement of healthcare monitoring by introducing a Hybrid Deep Learning model, combining DNN and RNN architectures, within the context of Software Defined Networking. The achieved high prediction accuracy in anomaly detection signifies a significant leap forward in the reliability and precision of healthcare monitoring systems, paving the way for more robust and responsive healthcare networks.