415
Scrivener Publishing
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Machine Learning in Biomedical Science and Healthcare Informatics
Series Editors: Vishal Jain and Jyotir Moy Chatterjee
In this series, the focus centres on the various applications of machine learning in the biomedical engineering and healthcare fields, with a special emphasis on the most representative learning techniques, namely deep learning-based approaches. Machine learning tasks typically classified into two broad categories depending on whether there is a learning “label” or “feedback” available to a learning system: supervised learning and unsupervised learning. This series also introduces various types of machine learning tasks in the biomedical engineering field from classification (supervised learning) to clustering (unsupervised learning). The objective of the series is to compile all aspects of biomedical science and healthcare informatics, from fundamental principles to current advanced concepts.
Submission to the series: Please send book proposals to [email protected] and/or [email protected]
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,
