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REVIEW

Review of Metaheuristic Optimization Techniques for Enhancing E-Health Applications

Qun Song1, Chao Gao1, Han Wu1, Zhiheng Rao1, Huafeng Qin1,*, Simon Fong1,2,*
1 Chongqing Intelligence Perception and Block Chain Technology Key Laboratory, The Department of Artificial Intelligent, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China
2 Department of Computer and Information Science, University of Macau, Taipa, Macau, 999078, China
* Corresponding Author: Huafeng Qin. Email: email; Simon Fong. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070918

Received 28 July 2025; Accepted 15 October 2025; Published online 07 November 2025

Abstract

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.

Keywords

Metaheuristic optimization; E-Health; disease diagnosis; medical resource optimization; complex optimization
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