TELE-HEALTHCARE
This book elucidates all aspects of tele-healthcare which is the application of AI, soft computing, digital information, and communication technologies, to provide services remotely and manage one’s healthcare.
Throughout the world, there are huge developing crises with respect to healthcare workforce shortages, as well as a growing burden of chronic diseases. As a result, e-health has become one of the fastest-growing service areas in the medical sector. E-health supports and ensures the availability of proper healthcare, public health, and health education services at a distance and in remote places. For the sector to grow and meet the need of the marketplace, e-health applications have become one of the fastest growing areas of research. However, to grow at a larger scale requires the following:
This book mainly focuses on these three points for the development and implementation of e-health services globally.
In this book the reader will find:
- Details of the challenges in promoting and implementing the telehealth industry.
- How to expand a globalized agenda of personalized telehealth in integrative medical treatment for disease diagnosis and its industrial transformation.
- How to design machine learning techniques for improving the tele-healthcare system.
Audience
Researchers and post-graduate students in biomedical engineering, artificial intelligence, and information technology; medical doctors and practitioners and industry experts in the healthcare sector; healthcare sector network administrators.
表中的内容
Preface xv
1 Machine Learning–Assisted Remote Patient Monitoring with Data Analytics 1
Vinutha D. C., Kavyashree and G. T. Raju
1.1 Introduction 2
1.1.1 Traditional Patient Monitoring System 2
1.1.2 Remote Monitoring System 3
1.1.3 Challenges in RPM 4
1.2 Literature Survey 5
1.2.1 Machine Learning Approaches in Patient Monitoring 7
1.3 Machine Learning in RPM 8
1.3.1 Support Vector Machine 9
1.3.2 Decision Tree 10
1.3.3 Random Forest 11
1.3.4 Logistic Regression 11
1.3.5 Genetic Algorithm 12
1.3.6 Simple Linear Regression 12
1.3.7 KNN Algorithm 13
1.3.8 Naive Bayes Algorithm 14
1.4 System Architecture 15
1.4.1 Data Collection 16
1.4.2 Data Pre-Processing 17
1.4.3 Apply Machine Learning Algorithm and Prediction 18
1.5 Results 21
1.6 Future Enhancement 23
1.7 Conclusion 24
References 24
2 A Survey on Recent Computer-Aided Diagnosis for Detecting Diabetic Retinopathy 27
Priyadharsini C., Jagadeesh Kannan R. and Farookh Khadeer Hussain
2.1 Introduction 28
2.2 Diabetic Retinopathy 28
2.2.1 Features of DR 28
2.2.2 Stages of DR 29
2.3 Overview of DL Models 31
2.3.1 Convolution Neural Network 31
2.3.2 Autoencoders 32
2.3.3 Boltzmann Machine and Deep Belief Network 32
2.4 Data Set 33
2.5 Performance Metrics 34
2.6 Literature Survey 36
2.6.1 Segmentation of Blood Vessels 36
2.6.2 Optic Disc Feature 49
2.6.3 Lesion Detections 50
2.6.3.1 Exudate Detection 50
2.6.3.2 MA and HM 51
2.6.4 DR Classification 51
2.7 Discussion and Future Directions 52
2.8 Conclusion 53
References 53
3 A New Improved Cryptography Method-Based e-Health Application in Cloud Computing Environment 59
Dipesh Kumar, Nirupama Mandal and Yugal Kumar
3.1 Introduction 60
3.1.1 Contribution 61
3.2 Motivation 62
3.3 Related Works 62
3.4 Challenges 64
3.5 Proposed Work 64
3.6 Proposed Algorithm for Encryption 66
3.6.1 Demonstration of Encryption Algorithm 66
3.6.1.1 When the Number of Columns Selected in the Table is Even 66
3.6.1.2 When the Number of Columns Selected in the Table is Odd 69
3.6.2 Flowchart for Encryption 72
3.7 Algorithm for Decryption 73
3.7.1 Demonstration of Decryption Algorithm 73
3.7.1.1 When the Number of Columns Selected in the Table is Even 73
3.7.1.2 When the Number of Columns Selected in the Table is Odd 75
3.7.2 Flowchart of Decryption Algorithm 78
3.8 Experiment and Result 78
3.9 Conclusion 80
References 80
4 Cutaneous Disease Optimization Using Teledermatology Underresourced Clinics 85
Supriya M., Murugan K., Shanmugaraja T. and Venkatesh T.
4.1 Introduction 86
4.2 Materials and Methods 87
4.2.1 Clinical Setting and Teledermatology Workflow 87
4.2.2 Study Design, Data Collection, and Analysis 87
4.3 Proposed System 88
4.3.1 Teledermatology in an Underresourced Clinic 88
4.3.2 Teledermatology Consultations from Uninsured Patients 89
4.3.3 Teledermatology for Patients Lacking Access to Dermatologists 90
4.3.4 Teledermatologist Management from Nonspecialists 92
4.3.5 Segment Factors of Referring PCPs and Their Patients 93
4.3.6 Teledermatology Operational Considerations 94
4.3.7 Instruction of PCPs 94
4.4 Challenges 95
4.5 Results and Discussion 95
4.5.1 Challenges of Referring to Teledermatology Services 96
References 98
5 Cognitive Assessment Based on Eye Tracking Using Device-Embedded Cameras via Tele-Neuropsychology 101
Shanmugaraja T., Venkatesh T., Supriya M. and Murugan K.
5.1 Introduction 102
5.2 Materials and Methods 102
5.3 Framework Elements 102
5.3.1 Eye Tracker Camera 102
5.3.2 Test Construction 103
5.3.3 Web Camera 106
5.3.4 Camera for Eye Tracking 106
5.4 Proposed System 106
5.4.1 Camera for Tracking Eye 106
5.4.2 Web Camera 108
5.4.3 Scoring 108
5.4.4 Eye Tracking Camera 108
5.4.5 Web Camera Human-Coded Scoring 108
5.5 Subjects 109
5.5.1 Characteristics of Subject 109
5.6 Methodology 110
5.6.1 Analysis of Data 110
5.7 Results 110
5.8 Discussion 112
5.9 Conclusion 114
References 115
6 Fuzzy-Based Patient Health Monitoring System 117
Venkatesh T., Murugan K., Supriya M., Shanmugaraja T. and Rekha Chakravarthi
6.1 Introduction 118
6.1.1 General Problem 119
6.1.2 Existing Patient Monitoring and Diagnosis Systems 119
6.1.3 Fuzzy Logic Systems 120
6.2 System Design 122
6.2.1 Hardware Requirements 122
6.2.1.1 Functional Requirements 123
6.2.1.2 Nonfunctional Specifications 125
6.3 Software Architecture 125
6.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API) 126
6.3.2 Flowchart—API 128
6.3.3 Foreign Tag IDs 129
6.3.4 Database Manager 130
6.3.5 Database Designing 130
6.3.6 The Fuzzy Logic System 131
6.3.6.1 Introduction to Fuzzy Logic 131
6.3.6.2 The Modified Prior Alerting Score (MPAS) 132
6.3.6.3 Structure of the Fuzzy Logic System 134
6.3.7 Designing a System in Fuzzy 135
6.3.7.1 Input Variables 135
6.3.7.2 The Output Variable 138
6.4 Results and Discussion 140
6.4.1 Hardware Sensors Validation 140
6.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine 141
6.4.3 Normal Group (NRM) 146
6.4.4 Low Risk Group 146
6.4.5 High Risk Group (HRG) 153
6.5 Conclusions and Future Work 155
6.5.1 Summary and Concluding Remarks 155
6.5.2 Future Directions 155
References 155
7 Artificial Intelligence: A Key for Detecting COVID-19 Using Chest Radiography 159
C. Vinothini, P. Anitha, Priya J., Abirami A. and Akash S.
7.1 Introduction 160
7.2 Related Work 162
7.2.1 Traditional Approach 162
7.2.2 Deep Learning–Based Approach 163
7.3 Materials and Methods 163
7.3.1 Data Set and Data Pre-Processing 163
7.3.2 Proposed Model 165
7.4 Experiment and Result 171
7.4.1 Experiment Setup 171
7.4.2 Comparison with Other Models 173
7.5 Results 174
7.6 Conclusion 175
References 176
8 An Efficient Io T Framework for Patient Monitoring and Predicting Heart Disease Based on Machine Learning Algorithms 179
Shanthi S., Nidhya R., Uma Perumal and Manish Kumar
8.1 Introduction 180
8.2 Literature Survey 182
8.3 Machine Learning Algorithms 183
8.4 Problem Statement 184
8.5 Proposed Work 185
8.5.1 Data Set Description 185
8.5.2 Collection of Values Through Sensor Nodes 186
8.5.3 Storage of Data in Cloud 187
8.5.4 Prediction with Machine Learning Algorithms 188
8.5.4.1 Data Cleaning and Preparation 188
8.5.4.2 Data Splitting 189
8.5.4.3 Training and Testing 189
8.5.5 Machine Learning Algorithms 189
8.5.5.1 Naive Bayes Algorithm 189
8.5.5.2 Decision Tree Algorithm 190
8.5.5.3 K-Neighbors Classifier 191
8.5.5.4 Logistic Regression 192
8.6 Performance Analysis and Evaluation 192
8.7 Conclusion 197
References 197
9 BABW: Biometric-Based Authentication Using DWT and FFNN 201
R. Kingsy Grace, M.S. Geetha Devasena and R. Manimegalai
9.1 Introduction 202
9.2 Literature Survey 203
9.3 BABW: Biometric Authentication Using Brain Waves 208
9.4 Results and Discussion 211
9.5 Conclusion 215
References 216
10 Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review 221
Pavithra D., Jayanthi A. N., Nidhya R. and Balamurugan S.
10.1 Introduction 222
10.2 Autism Screening Methods 223
10.2.1 Autism Screening Instrument for Educational Planning—3rd Version 224
10.2.2 Quantitative Checklist for Autism in Toddlers 224
10.2.3 Autism Behavior Checklist 224
10.2.4 Developmental Behavior Checklist-Early Screen 225
10.2.5 Childhood Autism Rating Scale Version 2 225
10.2.6 Autism Spectrum Screening Questionnaire (ASSQ) 226
10.2.7 Early Screening for Autistic Traits 226
10.2.8 Autism Spectrum Quotient 226
10.2.9 Social Communication Questionnaire 227
10.2.10 Child Behavior Check List 227
10.2.11 Indian Scale for Assessment of Autism 227
10.3 Machine Learning in ASD Screening and Diagnosis 228
10.4 DL in ASD Diagnosis 238
10.5 Conclusion 242
References 242
11 Drug Target Module Mining Using Biological Multifunctional Score-Based Coclustering 249
R. Gowri and R. Rathipriya
11.1 Introduction 249
11.2 Literature Study 250
11.3 Materials and Methods 253
11.3.1 Biological Terminologies 253
11.3.2 Functional Coherence 256
11.3.3 Biological Significances 257
11.3.4 Existing Approach: MR-Co C 257
11.4 Proposed Approach: MR-Co Cmulti 258
11.4.1 Biological Score Measures for DTM 259
11.4.2 Multifunctional Score-Based Co-Clustering Approach 259
11.5 Experimental Analysis 264
11.5.1 Experimental Results 265
11.6 Discussion 280
11.7 Conclusion 280
Acknowledgment 281
References 281
12 The Ascendant Role of Machine Learning Algorithms in the Prediction of Breast Cancer and Treatment Using Telehealth 285
Jothi K.R., Oswalt Manoj S., Ananya Singhal and Suruchi Parashar
12.1 Introduction 286
12.1.1 Objective 287
12.1.2 Description and Goals 287
12.1.2.1 Data Exploration 288
12.1.2.2 Data Pre-Processing 288
12.1.2.3 Feature Scaling 288
12.1.2.4 Model Selection and Evaluation 288
12.2 Literature Review 289
12.3 Architecture Design and Implementation 304
12.4 Results and Discussion 310
12.5 Conclusion 312
12.6 Future Work 313
References 314
13 Remote Patient Monitoring: Data Sharing and Prediction Using Machine Learning 317
Mohammed Hameed Alhameed, S. Shanthi, Uma Perumal and Fathe Jeribi
13.1 Introduction 318
13.1.1 Patient Monitoring in Healthcare System 318
13.2 Literature Survey 321
13.3 Problem Statement 322
13.4 Machine Learning 322
13.4.1 Introduction 322
13.4.2 Cloud Computing 324
13.4.3 Design and Architecture 325
13.5 Proposed System 326
13.6 Results and Discussions 331
13.7 Privacy and Security Challenges 333
13.8 Conclusions and Future Enhancement 334
References 335
14 Investigations on Machine Learning Models to Envisage Coronavirus in Patients 339
R. Sabitha, J. Shanthini, R.M. Bhavadharini and S. Karthik
14.1 Introduction 340
14.2 Categories of ML Algorithms in Healthcare 341
14.3 Why ML to Fight COVID-19? Tools and Techniques 343
14.4 Highlights of ML Algorithms Under Consideration 344
14.5 Experimentation and Investigation 349
14.6 Comparative Analysis of the Algorithms 353
14.7 Scope of Enhancement for Better Investigation 354
References 356
15 Healthcare Informatics: Emerging Trends, Challenges, and Analysis of Medical Imaging 359
G. Karthick and N.S. Nithya
15.1 Emerging Trends and Challenges in Healthcare Informatics 360
15.1.1 Advanced Technologies in Healthcare Informatics 360
15.1.2 Intelligent Smart Healthcare Devices Using Io T With DL 361
15.1.3 Cyber Security in Healthcare Informatics 362
15.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics 363
15.2 Performance Analysis of Medical Image Compression Using Wavelet Functions 364
15.2.1 Introduction 364
15.2.2 Materials and Methods 366
15.2.3 Wavelet Basis Functions 367
15.2.3.1 Haar Wavelet 367
15.2.3.2 db Wavelet 368
15.2.3.3 bior Wavelet 368
15.2.3.4 rbio Wavelet 368
15.2.3.5 Symlets Wavelet 369
15.2.3.6 coif Wavelet 369
15.2.3.7 dmey Wavelet 369
15.2.3.8 fk Wavelet 369
15.2.4 Compression Methods 370
15.2.4.1 Embedded Zero-Trees of Wavelet Transform 370
15.2.4.2 Set Partitioning in Hierarchical Trees 370
15.2.4.3 Adaptively Scanned Wavelet Difference Reduction 370
15.2.4.4 Coefficient Thresholding 371
15.3 Results and Discussion 371
15.3.1 Mean Square Error 371
15.3.2 Peak Signal to Noise Ratio 371
15.4 Conclusion 380
15.4.1 Summary 380
References 380
Index 383
关于作者
R. Nidhya, Ph D, is an assistant professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated to Jawaharlal Nehru Technical University, Anantapuram, India. She has published many research articles in SCI journals and her research interests include wireless body area networks, network security, and data mining.
Manish Kumar, Ph D, is an assistant professor in the School of Computer Science & Engineering, VIT Chennai. His research interests include soft computing applications for bioinformatics problems and computational intelligence.
S. Balamurugan, Ph D, is the Director of Research and Development, Intelligent Research Consultancy Services (i RCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.
来自同一作者的更多电子书 / 编辑
16,504 此类电子书
