A Comprehensive Review of Optimization Techniques for Healthcare Using Cancer Datasets

This study presents a systematic review of metaheuristic optimization techniques applied to healthcare problems using cancer datasets. A structured search of recently published peer-reviewed literature was carried out, focusing on five major application areas: feature selection, classification, image segmentation, hyperparameter tuning, and early detection. For each eligible study, the optimization strategy, dataset characteristics, data modality, learning model, validation protocol, and reported outcomes are provided. The reviewed works were organized into a taxonomy of original, modified, and hybridized algorithms, and a descriptive analysis was performed to assess algorithm prevalence and dataset utilization. The findings highlight that feature selection remains the most widely explored task, while hyperparameter tuning and image segmentation have gained increasing attention in recent years. Genetic Algorithms, Particle Swarm Optimization, Whale Optimization, and Grey Wolf Optimization emerged as the most frequently applied approaches, particularly when integrated with deep learning models for complex imaging tasks. Although these methods consistently outperformed non-optimized or grid-searched baselines, challenges remain regarding external validation, management of class imbalance, reproducibility, and transparent reporting of computational costs. Overall, this review provides a comprehensive synthesis of current practices and trends in optimization for cancer data analytics.