Machine Learning for Healthcare Applications. Группа авторов
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MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Machine Learning for Healthcare Applications
Edited by
Sachi Nandan Mohanty
G. Nalinipriya
Om Prakash Jena
Achyuth Sarkar
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
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119791812
Cover image: Pixabay.Com
Cover design by Russell Richardson
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
Machine learning is one of the principal components of computational methodology. In today’s highly integrated world, when solutions to problems are cross-disciplinary in nature, machine learning promises to become a powerful means for obtaining solutions to problems very quickly, yet accurately and acceptably.
When considering the idea of using machine learning in healthcare, it is a Herculean task to present before the reader the entire gamut of information in the field of intelligent systems. It was therefore our objective to keep the presentation narrow and intensive. The approach of this book is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.
Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.
This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
The chapters of the book are organized as follows:
Chapter 1 introduces the fundamental concepts of machine learning and its applications, and describes the setup used throughout the book. It is now realized that complex real-world problems require intelligent systems that combine knowledge, techniques and methodologies from various sources.
Chapter 2 describes the actual machine learning algorithms that are most widely used in practice, and discusses their advantages and shortcomings. It is therefore necessary to work through conventional machine learning algorithms while relating the underlying theme to cutting-edge neuroscience research findings.
Chapter 3 explains the study of neuromarketing with EEG signals and machine learning techniques. This is followed by a detailed review of the global function of classifiers and the inner workings. Such a premise provides the fabric for presentation of ideas throughout this text.
Chapter 4 elaborates on an expert system-based clinical decision support system for hepatitis B prediction and diagnosis. It develops a working model of the decision support system and its application domain. The clinical decision helps to improve the diagnostic performance.