Review of Metaheuristic Optimization Techniques for Enhancing E-Health Applications

Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the Chaotic Salp Swarm Algorithm (CSSA) achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia. Future research should progress in three directions: improving theoretical reliability and performance predictability in medical contexts; designing more adaptive and deployable mechanisms for real-world systems; and integrating ethical, privacy, and technological considerations to enable precision medicine, digital twins, and intelligent medical devices.