Nature-Inspired Optimization Methodologies in Biomedical and Healthcare

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.

List of contents

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.

Product details

• Assisted by: Sheryl Brahnam (Editor), Asit Kumar Das (Editor), Asit Kumar Das (Editor), Saroj K. Meher (Editor), Bighnaraj Naik (Editor), Bighnaraj Naik et al (Editor), Janmenjoy Nayak (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 15.11.2022
• EAN: 9783031175435
• ISBN: 978-3-0-3117543-5
• No. of pages: 293
• Dimensions: 155 mm x 20 mm x 235 mm
• Illustrations: XVIII, 293 p. 111 illus., 77 illus. in color.
• Series: Intelligent Systems Reference Library
• Subject: Natural sciences, medicine, IT, technology > Technology > General, dictionaries