Significance of computational intelligence and genetic programming based models for real-time disease monitoring in wireless sensor networks

Abstract

This review addresses the pressing challenge of improving disease monitoring through the integration of computational intelligence (CI) and genetic programming (GP) within wireless sensor networks (WSNs). The significance of this work lies in the rising incidence of communicable diseases and the demand for efficient, scalable monitoring systems. The study synthesizes existing literature on CI techniques, such as machine learning and neural networks, alongside GP to optimize disease monitoring protocols. Key findings indicate that these integrated approaches enhance the reliability and adaptability of monitoring systems in dynamic healthcare environments. The review concludes that employing stochastic modeling significantly mitigates uncertainties in disease dynamics, leading to more accurate real-time monitoring. The novelty of this work is its comprehensive framework that combines CI and GP, addressing limitations in traditional disease monitoring methods and proposing innovative solutions to challenges such as energy efficiency and data security. By fostering collaboration and innovation in WSN-based healthcare systems, this review contributes to advancing healthcare technologies and improving patient outcomes across various settings.