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Bioinformatics and Medical Applications
Big Data Using Deep Learning Algorithms
Edited by
A. Suresh
S. Vimal
Y. Harold Robinson
Dhinesh Kumar Ramaswami
and
R. Udendhran
This edition first published 2022 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
© 2022 Scrivener Publishing LLC
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-79183-6
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
This book features bioinformatics applications in the medical field that employ deep learning algorithms to analyze massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. In addition to delivering the various bioinformatics computational methods used to identify diseases at an early stage, it also collects cutting-edge resources in a single source designed to enlighten the reader with topics centered on computer science, mathematics, and biology. Since bioinformatics is critical for data management in the current fields of biology and medicine, this book explains the important tools used by bioinformaticians and examines how they are used to evaluate biological data in order to advance disease knowledge.
As shown in the chapter-by-chapter synopsis that follows, the editors of this book have curated a distinguished group of perceptive and concise chapters that reflect the current state of medical treatments and systems and offer emerging solutions for a more personalized approach to the healthcare field. Since applying deep learning techniques for data-driven solutions in health information allows automated analysis, this method can be more advantageous in addressing the problems arising from medical- and health-related information.
– Chapter 1, “Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction,” discusses the ensemble learning that overcomes the limitations of a single algorithm, such as bias and variance, by using a multitude of algorithms. It highlights the importance of ensemble techniques in improving the forecast accuracy and displaying an acceptable performance in disease prediction. Additionally, the authors have worked on a procedure to further improve the accuracy of the ensemble method post application by focusing on the wrongly classified records and using probabilistic optimization to select pertinent columns by increasing their weight and doing a reclassification which would result in further improved accuracy.
– Chapter 2, “Cancerous Cells Detection in Lung Organs of the Human Body: IoT-Based Healthcare 4.0 Approach,” analyzes three types of cancer—squamous cell carcinoma, adenocarcinoma, and large cell carcinoma—derived from lung tissue, and investigates how AI can customize treatment choices for lung cancer patients.
– Chapter 3, “Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case,” describes the main molecular features of SARS-CoV-2 that cause COVID-19 disease, as well as a high-efficiency computational prediction called the polarity index method. Furthermore, it presents a molecular classification of the RNA-virus and DNA-virus families with results obtained by the proposed non-supervised method focusing on the linear representation of proteins.
– Chapter 4, “Deep Learning in Gait Abnormality Detection: Principles and Illustrations,” discusses cerebral palsy, a medical condition
