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.
