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Ankur Saxena (editor) - Artificial Intelligence and Machine Learning in Healthcare

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Ankur Saxena (editor) Artificial Intelligence and Machine Learning in Healthcare

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This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

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Ankur Saxena (editor) - Artificial Intelligence and Machine Learning in Healthcare
Artificial Intelligence and Machine Learning in Healthcare
Ankur Saxena (editor)
Artificial Intelligence and Machine Learning in Healthcare

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Book cover of Artificial Intelligence and Machine Learning in Healthcare - photo 1
Book cover of Artificial Intelligence and Machine Learning in Healthcare
Ankur Saxena and Shivani Chandra
Artificial Intelligence and Machine Learning in Healthcare
1st ed. 2021
Logo of the publisher Ankur Saxena Amity University Noida Uttar Pradesh - photo 2
Logo of the publisher
Ankur Saxena
Amity University, Noida, Uttar Pradesh, India
Shivani Chandra
Amity University, Noida, Uttar Pradesh, India
ISBN 978-981-16-0810-0 e-ISBN 978-981-16-0811-7
https://doi.org/10.1007/978-981-16-0811-7
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.

The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents
List of Figures
List of Tables
About the Authors
Ankur Saxena

is currently working as an Assistant Professor at Amity University, Noida, Uttar Pradesh. He has been teaching graduate and post-graduate students for more than 15 years and has 3 years of industrial experience in software development. He has published 5 books and more than 40 research articles in reputed journals and is an editorial board member and reviewer for several journals. His research interests include cloud computing, big data, machine learning, evolutionary algorithms, software frameworks, design and analysis of algorithms, and biometric identification.

Shivani Chandra

is an Assistant Professor at Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Noida. She has more than 20 years of experience in biotechnology and molecular biology. Her research interests include genomics analysis, computational biology, and bioinformatics data analysis. She has submitted more than 4000 clones to the NCBI GenBank and was one of the key players in the Rice Genome Sequencing Project. She has published several research articles in genome sequencing, comparative genomics, and genome analysis in reputed journals. She has more than 15 years of teaching experience in computational biology, molecular biology, genetics, recombinant DNA technology, and bioinformatics.

The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. Saxena, S. Chandra Artificial Intelligence and Machine Learning in Healthcare https://doi.org/10.1007/978-981-16-0811-7_1
1. Practical Applications of Artificial Intelligence for Disease Prognosis and Management
Ankur Saxena
(1)
Amity University, Noida, Uttar Pradesh, India
Abstract

Artificial intelligence (AI) is an emerging field, which provides enhanced capabilities of decision-making to the machines. The extremely popular application of machine learning approaches in the area of disease prognosis and management is the precision medicine, which can be described as deciding the best treatment options based on features, such as attributes of the patients and the treatment undertaken. By knowing the hidden pattern of the data and its knowledge, computers can predict the future events. Thus, it helps the machine to learn effortlessly without any human intervention and makes easy to do complicated decision- making process. The objective of this chapter is to comprehend and explore the applications of artificial intelligence for the better management of the early prognosis and treatment protocols for diseases. The focus of the chapter will be towards the application of artificial intelligence techniques to medical data management. These techniques can analyse different types of data retrieved from patient samples, such as structured images, features based on patient vitals for predicting the probability of the outcome of a disease and design a better treatment protocol.

Keywords
Artificial intelligence Disease management Disease prognosis MATLAB Predictive modelling
1.1 Overview of Application of AI in Disease Management
Artificial intelligence (AI) is an intelligence technology that is artificially programmed by humans to mimic like human. This artificial intelligence gets integrated with computer system that is called AI system, which ultimately functions as the thinking machine (Wu ).
Fig 11 Artificial intelligence machine learning and deep learning The - photo 3
Fig. 1.1

Artificial intelligence, machine learning, and deep learning

The machine learning (ML) is the subset of an artificial intelligence that helps a computer/system to learn from the environment automatically without any human intervention and applies that learning to make better decisions. Machine learning uses its various algorithms or techniques to learn, characterize and improve the data, so that it predicts better outcomes. The ML techniques/algorithms find the patterns first and then perform the action based on these patterns. Machine learning can be classified under four categories: (a) supervised learning, (b) unsupervised learning, (c) semi-supervised learning and d) reinforcement learning.

Supervised Learning
Supervised learning can be defined to be a type of machine learning, where both the input and the output is provided to the system (Akella ). The algorithm works by training the labelled data in a manner that the machine is able to learn and develop patterns between the input and the output data. It finds the pattern that tell us how we can categorize or classify datapoints in data. The labelled data means known description, which is given to instances of data. For example, there are 20 different people who have different symptoms with cancer test results. According to the test results, we can place a tag or label to each patient, whether he/she is cancer positive or negative. Hence, the labelled data provides a shape to output. So, the process of supervised learning signifies that the machine will learn the pattern and classify the data. Same patterns can be used to find the unseen data. Supervised learning can be split into two forms:
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