This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.

Presents recent research on nature-inspired optimization methodologies in biomedical and health care Covers advanced methodologies, challenges, and solutions to diversified healthcare issues Presents applicable advanced mechanisms, automated tools, and possible approaches for solving biomedical problems

Inhalt
Nature-Inspired Optimization Algorithms: Past to Present.- Preventing the early spread of infectious diseases using Particle Swarm Optimization.- Optimized gradient boosting tree-based model for obesity level prediction from patient's physical condition and eating habits.