Performance Analysis of Nature-Inspired Optimization Algorithms for Chronic Disease Prediction

Chronic Diseases have spread speedily over the last one and a half decades; these life-threatening chronic diseases have a significantly higher rate of morbidity and fatality. Illness diagnosis using machine learning methods helps to decrease the percentage of fatality. On the other hand, Optimization algorithms address a broad range of problems while being flexible and adaptive. Such nature-inspired optimization algorithms are Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), and many more. These methods have been employed in the early prediction of many illnesses. This article investigates the effectiveness of several nature-inspired optimization strategies in diagnosing chronic illness. Compared to ACO, GA, and ABC algorithms, PSO has been widely used in an illness diagnosis. Furthermore, combining optimization approaches yields better results than using them separately.