Open Access

REVIEW

Review of Metaheuristic Optimization Techniques for Enhancing E-Health Applications

Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

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 Authors: Huafeng Qin. Email: ; Simon Fong. Email:

Computers, Materials & Continua 2026, 86(2), 1-49. https://doi.org/10.32604/cmc.2025.070918

Received 28 July 2025; Accepted 15 October 2025; Issue published 09 December 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.

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Keywords

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