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E-grāmata: Machine Learning in Healthcare: Fundamentals and Recent Applications [Taylor & Francis e-book]

(IIITB, India), (Department of Biomedical Engineering, National Institute of Technology Raipur, Chhattisgarh, India)
  • Formāts: 226 pages, 13 Tables, black and white; 69 Line drawings, black and white; 24 Halftones, black and white; 93 Illustrations, black and white
  • Izdošanas datums: 18-Feb-2022
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003097808
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  • Taylor & Francis e-book
  • Cena: 128,96 €*
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Machine Learning in Healthcare: Fundamentals and Recent ApplicationsPalielināt
  • Formāts: 226 pages, 13 Tables, black and white; 69 Line drawings, black and white; 24 Halftones, black and white; 93 Illustrations, black and white
  • Izdošanas datums: 18-Feb-2022
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003097808
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9781003097808
Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research.

Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.

This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.
List of Figures
xiii
List of Tables
xvii
Preface xix
Acknowledgments xxiii
Author Bio xxv
Chapter 1 Biostatistics
1(22)
1.1 Data and Variables
1(1)
1.2 Types of Research Studies
2(1)
1.3 Sources of Medical Data
2(1)
1.4 Measures of Central Tendency
3(2)
1.5 Data Sampling and Its Types
5(1)
1.5.1 Probability Sampling Methods
5(1)
1.5.2 Non-probability Sampling Methods
6(1)
1.6 Statistical Significance Analysis
6(4)
1.7 Skewness
10(1)
1.8 Kurtosis
11(3)
1.8.1 Mesokurtic
13(1)
1.8.2 Leptokurtic
13(1)
1.8.3 Platykurtic
14(1)
1.9 Curve Fitting
14(1)
1.9.1 Linear and Non-linear Relationship
14(1)
1.9.2 Use of Curve-Fitting Method
14(1)
1.10 Correlation
14(4)
1.10.1 Pearson Correlation (PC)
15(1)
1.10.2 Spearman Rank Correlation (SRC)
16(2)
1.11 Regression
18(5)
1.11.1 Linear Regression
18(1)
1.11.2 Estimation of Regression Coefficients
19(4)
Chapter 2 Probability Theory
23(12)
2.1 Basic Concept of Probability
23(1)
2.2 Random Experiment
24(1)
2.3 Conditional Probability
24(2)
2.3.1 Types of Events
25(1)
2.4 Bayes Theorem
26(2)
2.5 Random Variable
28(1)
2.6 Distribution Functions
28(3)
2.6.1 Binomial Distribution
29(1)
2.6.2 Poisson Distribution
30(1)
2.6.3 Normal Distribution
30(1)
2.7 Estimation
31(1)
2.8 Standard Error
32(1)
2.9 Probability of Error
32(3)
Chapter 3 Medical Data Acquisition and Pre-processing
35(10)
3.1 Medical Data Formats
35(3)
3.1.1 Data Formats for Medical Images
35(1)
3.1.1.1 DICOM (Digital Imaging and Communications in Medicine)
36(1)
3.1.1.2 Analyse
36(1)
3.1.1.3 NlfTI (Neuroimaging Informatics Technology Initiative)
36(1)
3.1.1.4 MINC (Medical Imaging NetCDF)
36(1)
3.1.2 Medical Data Formats for Signals
37(1)
3.1.2.1 EDF (European Data Format)
37(1)
3.1.2.2 BDF (BioSemi Data Format)
37(1)
3.1.2.3 GDF (General Data Format)
38(1)
3.2 Data Augmentation and Generation
38(1)
3.3 Data Labelling
38(1)
3.4 Data Cleaning
39(3)
3.4.1 Statistical Approach
40(1)
3.4.1.1 Listwise Deletion
40(1)
3.4.1.2 Pairwise Deletion
40(1)
3.4.1.3 Multiple Imputation
40(1)
3.4.1.4 Maximum Likelihood Imputation
41(1)
3.4.2 Machine Learning for Data Imputation
41(1)
3.4.2.1 K-Nearest Neighbour (KNN)
41(1)
3.4.2.2 Bayesian Network (BN)
41(1)
3.5 Data Normalization
42(3)
Chapter 4 Medical Image Processing
45(20)
4.1 Medical Image Modalities, Their Applications, Advantages and Limitations
45(4)
4.1.1 Radiography
46(1)
4.1.2 Nuclear Medicine
46(1)
4.1.2.1 Positron Emission Tomography (PET)
46(1)
4.1.3 Elastography
46(1)
4.1.4 Photoacoustic Imaging
47(1)
4.1.5 Tomography
47(1)
4.1.6 Magnetic Resonance Imaging (MRI)
47(1)
4.1.7 Ultrasound Imaging Techniques
48(1)
4.2 Medical Image Enhancement
49(2)
4.3 Basics of Histogram
51(5)
4.4 Medical Image De-noising
56(3)
4.4.1 Spatial Filtering
56(1)
4.4.1.1 Linear Filters
56(2)
4.4.1.2 Non-linear Filters
58(1)
4.4.2 Transform Domain Filtering
58(1)
4.4.2.1 Non-data Adaptive Transform
58(1)
4.4.2.2 Data-Adaptive Transforms
59(1)
4.5 Segmentation
59(1)
4.6 Region-Based Methods
59(6)
4.6.1 Region-Growing Segmentation
61(4)
Chapter 5 Bio-signals
65(12)
5.1 Origin of Bio-signals
65(1)
5.2 Different Types of Bio-signals
65(7)
5.2.1 Electrocardiogram
65(3)
5.2.2 Electroencephalogram (EEG)
68(1)
5.2.3 Electroocculogram (EOG)
69(1)
5.2.4 Electromyogram (EMG)
69(3)
5.3 Noise and Artefacts
72(1)
5.4 Filtering of Bio-signals
73(1)
5.5 Applications of Bio-signals
74(3)
Chapter 6 Feature Extraction
77(30)
6.1 Feature Extraction
77(1)
6.2 Echographic Characteristics of Breast Tumours in Ultrasound Imaging
78(1)
6.3 Texture Feature Extraction
78(22)
6.3.1 First-Order Statistical Features
78(4)
6.3.2 Grey-Level Co-occurrence Matrices
82(5)
6.3.3 Grey-Level Difference Statistics
87(1)
6.3.4 Neighbourhood Grey-Tone Difference Matrix
88(3)
6.3.5 Statistical Feature Matrix
91(1)
6.3.6 Texture Energy Measures
92(1)
6.3.7 Fractal Dimension Texture Analysis
93(2)
6.3.8 Spectral Measures of Texture
95(1)
6.3.9 Run-Length Texture Features
96(4)
6.4 Shape Feature Extraction
100(2)
6.4.1 Region Properties
100(1)
6.4.2 Moment Invariants
100(2)
6.5 Feature Normalization
102(5)
6.5.1 Brief Overview of Feature Normalization Techniques
102(5)
Chapter 7 Introduction to Machine Learning
107(28)
7.1 Introduction: What Is Machine Learning?
107(1)
7.2 Classification of Machine Learning (ML) Methods
108(1)
7.3 Steps in Implementation of Machine Learning
109(4)
7.4 Training, Testing and Validation
113(1)
7.5 Machine Learning Methods
114(16)
7.5.1 Supervised Learning
114(11)
7.5.2 Unsupervised Learning
125(5)
7.6 Performance Evaluation of Machine Learning Model
130(5)
Chapter 8 Cancer Detection: Breast Cancer Detection Using Mammography, Ultrasound and Magnetic Resonance Imaging (MRI)
135(18)
8.1 Introduction
135(1)
8.2 Different Imaging Modalities
136(5)
8.2.1 Mammography (MG)
136(1)
8.2.2 Ultrasound (US)
137(3)
8.2.3 Magnetic Resonance Imaging (MRI)
140(1)
8.3 Breast Imaging Reporting and Data System (BI-RADS)
141(1)
8.4 Usefulness of Machine Learning (ML)
141(6)
8.4.1 Image Pre-processing
142(1)
8.4.2 Image Segmentation
143(1)
8.4.3 Feature Extraction
143(2)
8.4.4 Feature Selection
145(1)
8.4.5 Classification
146(1)
8.4.6 Performance Evaluation
147(1)
8.5 Issues and Challenges
147(2)
8.6 Conclusion
149(4)
Chapter 9 Sickle Cell Disease Management: A Machine Learning Approach
153(18)
9.1 Introduction
153(2)
9.2 Severity Detection of Sickle Cell Disease
155(4)
9.2.1 Analysis of Clinical Complications
156(1)
9.2.2 Analysis of Clinical Attributes
156(2)
9.2.3 Analysis of Microscopic Images of RBC
158(1)
9.3 Hydroxyurea Dosage Prediction for SCD Patients
159(6)
9.4 Patient Response to Medications through Hydroxyurea (HU)
165(3)
9.5 SCD Management Proposed Model
168(1)
9.6 Conclusions
168(3)
Chapter 10 Detection of Pulmonary Disease
171(20)
10.1 Introduction to Pulmonary Disorders
171(1)
10.2 Restrictive and Obstructive Lung Diseases
172(2)
10.2.1 Obstructive Lung Disease
172(1)
10.2.2 Restrictive Lung Disease
173(1)
10.3 Diagnosis of Disease and Disorder
174(1)
10.4 Chest X-ray
175(1)
10.5 CTScan
176(3)
10.6 SpO2 Level
179(1)
10.7 Arterial Blood Gas Analysis
180(1)
10.8 Laboratory Tests
180(1)
10.9 Bronchoscopy
181(1)
10.10 Sputum Test
182(1)
10.11 Pulmonary Function Test
183(2)
10.12 Challenges and Issues
185(1)
10.13 Application of Machine Learning in Diagnosis of Pulmonary Disorder
186(2)
10.14 Conclusion
188(3)
Chapter 11 Mental Illness and Neurodevelopmental Disorders
191(22)
11.1 Neurodevelopmental Disorders
191(1)
11.2 Developmental Dyslexia
191(4)
11.2.1 Diagnostic Methods
192(1)
11.2.2 Behavioural Method
192(1)
11.2.3 Brain Imaging Modalities
193(1)
11.2.4 Recent Advancement in Diagnostic Techniques
193(2)
11.3 Attention-Deficit/Hyperactivity Disorder (ADHD)
195(3)
11.3.1 Types
195(1)
11.3.2 Symptoms
195(1)
11.3.3 ADHD Screening
196(1)
11.3.4 Diagnosis Based on Brain Imaging and Machine Learning Methods
196(1)
11.3.5 Treatment for ADHD
197(1)
11.4 Parkinson's Disease
198(5)
11.4.1 Parkinson's Disease Prognosis and Measurement Rating Scales
199(1)
11.4.1.1 HY Scale
199(1)
11.4.1.2 UPDRS Scale
199(1)
11.4.2 Involvement of Digital Technologies for Detection and Monitoring of PD
200(3)
11.5 Epilepsy
203(2)
11.5.1 Recent Literatures on Epilepsy Detection
203(1)
11.5.2 Generalized Machine Learning Model for Epilepsy Detection System
204(1)
11.6 Schizophrenia
205(8)
11.6.1 Recent Research
206(1)
11.6.2 A Machine Learning Model for Schizophrenia Detection
206(7)
Chapter 12 Applications and Challenges
213(12)
12.1 Role of Machine Learning in Healthcare Research
213(1)
12.2 Efficient Diagnosis of Diabetes
214(1)
12.3 Neuropathy
215(1)
12.4 Drug Monitoring
216(1)
12.5 Bioinformatics
217(2)
12.6 DNA Analysis
219(1)
12.7 Digital Health Records
220(1)
12.8 Future Research Challenges
221(4)
Index 225
Dr Bikesh Kumar Singh is Assistant Professor in the Department of Biomedical Engineering at the National Institute of Technology Raipur, India, where he also received his Ph.D. in Biomedical Engineering. He has twelve years of teaching experience, and for five years he served as the Head of the Department of Biomedical Engineering. He has published more than seventy research papers in various international journals and conferences. He is the recipient of the Chhattisgarh Young Scientist Award, IETE Gowri Memorial Award, IEI Young Engineer Award.

Dr G.R. Sinha is Adjunct Professor at the International Institute of Information Technology Bangalore (IIITB) and deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar. He has been Visiting Professor (Honorary) in Sri Lanka Technological Campus Colombo during 2019-2020. He has been Visiting Professor for teaching Short Graduate Course on Cognitive Science and Brain Computing Research at University of Sannio Italy during September 2020-March 2021.

He has published 275 research papers, book chapters and books at International level that includes Biometrics published by Wiley India, a subsidiary of John Wiley; Medical Image Processing published by Prentice Hall of India and 13 Edited books. He is Associate Editor of five SCI/Scopus indexed journals. He has teaching and research experience of 22 years. He has been Dean of Faculty and Executive Council Member of CSVTU and currently a member of Senate of MIIT. Dr Sinha has been delivering ACM lectures as ACM Distinguished Speaker in the field of DSP since 2017 across the world. His few more important assignments include Expert Member for Vocational Training Program by Tata Institute of Social Sciences (TISS) for Two Years (2017-2019); Chhattisgarh Representative of IEEE MP Sub-Section Executive Council (2014-2017); Distinguished Speaker in the field of Digital Image Processing by Computer Society of India (2015). He served as Distinguished IEEE Lecturer in IEEE India council for Bombay section.

He is recipient of more than 12 awards and recognitions at National and International levels. He has delivered more than 50 Keynote/Invited Talks and Chaired many Technical Sessions in International Conferences across the world such as Singapore, Myanmar, Sri Lanka, Irvine, Italy and India. He is Consultant of various Skill Development initiatives of NSDC, Govt. of India. He is regular Referee of Project Grants under DST-EMR scheme and several other schemes of Govt. of India.
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