Table Of Content

i Machine Learning in Healthcare ii iii Machine Learning in Healthcare Fundamentals and Recent Applications Bikesh Kumar Singh G.R. Sinha iv First edition published 2022 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487- 2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2022 Bikesh Kumar Singh and G.R. Sinha Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978- 750- 8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Singh, Bikesh Kumar, author. | Sinha, G. R., 1975– author. Title: Machine learning in healthcare : fundamentals and recent applications / Bikesh Kumar Singh, G.R. Sinha. Description: First edition. | Boca Raton : CRC Press, 2022. | Includes bibliographical references and index. Identifiers: LCCN 2021042203 (print) | LCCN 2021042204 (ebook) | ISBN 9780367564421 (hardback) | ISBN 9780367564438 (paperback) | ISBN 9781003097808 (ebook) Subjects: MESH: Machine Learning | Delivery of Health Care Classification: LCC R855.3 (print) | LCC R855.3 (ebook) | NLM W 26.55.A7 | DDC 610.285–dc23 LC record available at https://lccn.loc.gov/2021042203 LC ebook record available at https://lccn.loc.gov/2021042204 ISBN: 978- 0- 367- 56442- 1 (hbk) ISBN: 978- 0- 367- 56443- 8 (pbk) ISBN: 978- 1- 003- 09780- 8 (ebk) DOI: 10.1201/ 9781003097808 Typeset in Times by Newgen Publishing UK v Dedicated to my late father, Shri R. S. Singh Ji, and my PhD joint supervisor, the late Dr. Kesari Verma Bikesh Kumar Singh Dedicated to my late grandparents, my teachers and Revered Swami Vivekananda G. R. Sinha vi vii Contents List of Figures .........................................................................................................xiii List of Tables ..........................................................................................................xvii Preface .....................................................................................................................xix Acknowledgments .................................................................................................xxiii Author Bio .............................................................................................................xxv Chapter 1 Biostatistics ..........................................................................................1 1.1 Data and Variables......................................................................1 1.2 Types of Research Studies .........................................................2 1.3 Sources of Medical Data ............................................................2 1.4 Measures of Central Tendency ...................................................3 1.5 Data Sampling and Its Types .....................................................5 1.5.1 Probability Sampling Methods .....................................5 1.5.2 Non- probability Sampling Methods .............................6 1.6 Statistical Significance Analysis ................................................6 1.7 Skewness ..................................................................................10 1.8 Kurtosis ....................................................................................11 1.8.1 Mesokurtic ..................................................................13 1.8.2 Leptokurtic .................................................................13 1.8.3 Platykurtic ..................................................................14 1.9 Curve Fitting ............................................................................14 1.9.1 Linear and Non- linear Relationship ...........................14 1.9.2 Use of Curve- Fitting Method .....................................14 1.10 Correlation ...............................................................................14 1.10.1 Pearson Correlation (PC)............................................15 1.10.2 Spearman Rank Correlation (SRC) ............................16 1.11 Regression ................................................................................18 1.11.1 Linear Regression .......................................................18 1.11.2 Estimation of Regression Coefficients .......................19 Chapter 2 Probability Theory ..............................................................................23 2.1 Basic Concept of Probability ...................................................23 2.2 Random Experiment ................................................................24 2.3 Conditional Probability ............................................................24 2.3.1 Types of Events ..........................................................25 2.4 Bayes Theorem ........................................................................26 2.5 Random Variable ......................................................................28 2.6 Distribution Functions..............................................................28 2.6.1 Binomial Distribution .................................................29 2.6.2 Poisson Distribution ...................................................30 vii viii viii Contents 2.6.3 Normal Distribution ...................................................30 2.7 Estimation ................................................................................31 2.8 Standard Error ..........................................................................32 2.9 Probability of Error ..................................................................32 Chapter 3 Medical Data Acquisition and Pre- processing ...................................35 3.1 Medical Data Formats ..............................................................35 3.1.1 Data Formats for Medical Images ..............................35 3.1.1.1 DICOM (Digital Imaging and Communications in Medicine) ....................36 3.1.1.2 Analyse ........................................................36 3.1.1.3 NIfTI (Neuroimaging Informatics Technology Initiative) .................................36 3.1.1.4 MINC (Medical Imaging NetCDF) .............36 3.1.2 Medical Data Formats for Signals ..............................37 3.1.2.1 EDF (European Data Format) .....................37 3.1.2.2 BDF (BioSemi Data Format) ......................37 3.1.2.3 GDF (General Data Format) .......................38 3.2 Data Augmentation and Generation .........................................38 3.3 Data Labelling ..........................................................................38 3.4 Data Cleaning ...........................................................................39 3.4.1 Statistical Approach ....................................................40 3.4.1.1 Listwise Deletion ........................................40 3.4.1.2 Pairwise Deletion ........................................40 3.4.1.3 Multiple Imputation ....................................40 3.4.1.4 Maximum Likelihood Imputation ...............41 3.4.2 Machine Learning for Data Imputation ......................41 3.4.2.1 K- Nearest Neighbour (KNN) ......................41 3.4.2.2 Bayesian Network (BN) ..............................41 3.5 Data Normalization ..................................................................42 Chapter 4 Medical Image Processing..................................................................45 4.1 Medical Image Modalities, Their Applications, Advantages and Limitations.....................................................45 4.1.1 Radiography ...............................................................46 4.1.2 Nuclear Medicine .......................................................46 4.1.2.1 Positron Emission Tomography (PET) .......46 4.1.3 Elastography ...............................................................46 4.1.4 Photoacoustic Imaging ...............................................47 4.1.5 Tomography ................................................................47 4.1.6 Magnetic Resonance Imaging (MRI) .........................47 4.1.7 Ultrasound Imaging Techniques .................................48 4.2 Medical Image Enhancement ...................................................49 4.3 Basics of Histogram .................................................................51 ix Contents ix 4.4 Medical Image De- noising .......................................................56 4.4.1 Spatial Filtering ..........................................................56 4.4.1.1 Linear Filters ..............................................56 4.4.1.2 Non- linear Filters ........................................58 4.4.2 Transform Domain Filtering.......................................58 4.4.2.1 Non- data Adaptive Transform .....................58 4.4.2.2 Data- Adaptive Transforms ..........................59 4.5 Segmentation ............................................................................59 4.6 Region- Based Methods ............................................................59 4.6.1 Region- Growing Segmentation ..................................61 Chapter 5 Bio- signals ..........................................................................................65 5.1 Origin of Bio- signals ................................................................65 5.2 Different Types of Bio- signals .................................................65 5.2.1 Electrocardiogram ......................................................65 5.2.2 Electroencephalogram (EEG).....................................68 5.2.3 Electroocculogram (EOG) ..........................................69 5.2.4 Electromyogram (EMG).............................................69 5.3 Noise and Artefacts ..................................................................72 5.4 Filtering of Bio- signals ............................................................73 5.5 Applications of Bio- signals ......................................................74 Chapter 6 Feature Extraction ..............................................................................77 6.1 Feature Extraction ....................................................................77 6.2 Echographic Characteristics of Breast Tumours in Ultrasound Imaging .................................................................78 6.3 Texture Feature Extraction .......................................................78 6.3.1 First- Order Statistical Features ...................................78 6.3.2 Grey- Level Co- occurrence Matrices ..........................82 6.3.3 Grey- Level Difference Statistics ................................87 6.3.4 Neighbourhood Grey- Tone Difference Matrix ...........88 6.3.5 Statistical Feature Matrix ...........................................91 6.3.6 Texture Energy Measures ...........................................92 6.3.7 Fractal Dimension Texture Analysis ..........................93 6.3.8 Spectral Measures of Texture .....................................95 6.3.9 Run- Length Texture Features .....................................96 6.4 Shape Feature Extraction .......................................................100 6.4.1 Region Properties .....................................................100 6.4.2 Moment Invariants ....................................................100 6.5 Feature Normalization ...........................................................102 6.5.1 Brief Overview of Feature Normalization Techniques ................................................................102