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Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Computational Intelligence and Healthcare Informatics
Edited by
Om Prakash Jena
Alok Ranjan Tripathy
Ahmed A. Elngar
Zdzislaw Polkowski
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
© 2021 Scrivener Publishing LLC
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-81868-7
Cover image: Pixabay.Com Cover design by Russell Richadson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analysing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments.
This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis.
The book aims to integrate several aspects of CI, like machine learning and deep learning, from diversified perspectives involving recent research trends and advanced topics in the field which will be of interest to academicians and researchers working in this area. The purpose of the book is to endow different communities with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modeling, advanced deployment, case studies, analytical results, computational structuring and significance progress in the field of machine learning and deep learning in healthcare applications. This book is targeted towards scientists, application doctors, health professionals, professors, researchers and students. Different dimensions of CI applications will be revealed and its use as a solution for assorted real-world biomedical and healthcare problems is illustrated. Following is a brief description of the subjects covered in each chapter.
– Chapter 1 is a systematic review of better options in the field of healthcare using machine learning and big data. The use of machine learning (ML) and big data in several application areas in healthcare services are identified which can further improve the unresolved challenges. Technologies such as ML will greatly transform traditional healthcare services and improve the relationship between service users and providers, providing better service in less time. Moreover, ML will help in keeping an eye on critical patients in real time, diagnose their disease, and recommend further treatment.
– Chapter 2 provides a critical analysis of the deep learning techniques utilized for thoracic image analysis and the respective accuracy achieved by it. Various deep learning techniques are described along with dataset, activation function and model used, number and types of layers used, learning rate, training time, epoch, performance metric, hardware used and type of abnormality detected.