Intelligent Healthcare Optimization Using Metaheuristic

Algorithms: A Review of Emerging Methods and Applications

Arian Rabet1,* Ehsan Khodadadi 2

1 Department of Integrative Biology, College of Letters and Science, University of California, Berkeley, Berkeley, CA, USA

2 Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR, 72701, USA

Received: February 27, 2026 Revised: April 22, 2026 Accepted: June 15, 2026 ⋆ Corresponding author

ABSTRACT

Machine learning and optimization techniques have significantly changed the healthcare industry, especially in finding

and managing essential and dangerous diseases like lung cancer, breast cancer, diabetes as well as heart disease.

Lung cancer, which is among the common fatal cancers, requires proper subtyping before proper management is

made. This has been achieved through machine learning alongside radiomics, where detailed imaging characteristics

of the tumor from CT scans are retrieved without invasive procedures. In the same way, machine learning has

provided much higher detection, diagnosis and treatment levels of breast cancer, diabetes and heart disease. This

literature review sums up the priorities of studies showing the benefits of using machine learning and bio-inspired

optimization methods to address the challenges posed by disease classification and prediction. Such complications

have proved great potential in improving the diagnostic methods used for early intervention and, thereby, accurate

and efficient diagnosis of a problem, developing an appropriate treatment plan and, thus, improving the patient caring

methods and scenario, which has played an imperative role determining the future of modern-day health care.

Keywords: Machine learning Optimization Healthcare industry Metaheuristic algorithms

1. INTRODUCTION

Metaheuristic algorithms today constitute an incredibly significant

aspect of solving optimization problems in many

disciplines, including healthcare. Contemporary healthcare

systems face many complex matters related to diagnostic accuracy,

asset allocation, data storage, medical images, transport,

and archiving. These systems’ needs call for malleable

approaches, which may scan through stochastic contexts and

handle huge amounts of information to make nearly real-time

decisions, which most traditional optimization techniques

cannot do. Metaheuristics offers a broad, steady means for

solving these problems, which are also able to avoid local

optima and work within large-size complex spaces. Because

they involve time, speed, and efficiency, even a minor improvement

reduces the time spent. Wrong outcomes can

significantly convert a patient’s fate and the ambiance functionality

of a venue.

Metaheuristics can also be applied in user authentication by

keystroke dynamics, where metaheuristic algorithms have

been used with other machine learning techniques to enhance

the security of mobile health applications. The benefits of

this method include boosting security while at the same time

protecting the privacy and integrity of health information on

smartphones [1]. The second example is applying adaptive

mutation dipper-throated optimization with transfer learning

to classify roads for self-driven cars. Notably, while this work

mainly concerns itself with transport, the result has critical

relevance in health care: improvement of the efficiency of the

transport system of ambulances, for example, can save lives