John Wiley & Sons Computational Intelligence and Healthcare Informatics Cover In den 21 Kapiteln dieses Buches werden verschiedene Aspekte der Computerintelligenz wie maschinelle.. Product #: 978-1-119-81868-7 Regular price: $195.33 $195.33 Auf Lager

Computational Intelligence and Healthcare Informatics

Jena, Om Prakash / Tripathy, Alok Ranjan / Elngar, Ahmed A. / Polkowski, Zdzislaw (Herausgeber)

Machine Learning in Biomedical Science and Healthcare Informatics

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1. Auflage Februar 2022
432 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-81868-7
John Wiley & Sons

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In den 21 Kapiteln dieses Buches werden verschiedene Aspekte der Computerintelligenz wie maschinelles Lernen und Deep Learning aus unterschiedlichen Perspektiven betrachtet. Mit dem Werk sollen die Fachleute verschiedener Bereiche einen Überblick über die innovativen Fortschritte erhalten, die in der Theorie, bei analytischen Ansätzen, numerischer Simulation, statistischer Analyse, Modellierung, fortschrittlicher Anwendung, Fallstudien, analytischen Ergebnissen und computergestützter Strukturierung gemacht wurden sowie über den beträchtlichem Fortschritt bei Anwendungen im Gesundheitswesen.

Preface xv

Part I: Introduction 1

1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3
Nahid Sami and Asfia Aziz

1.1 Introduction 3

1.2 Machine Learning in Healthcare 4

1.3 Machine Learning Algorithms 6

1.3.1 Supervised Learning 6

1.3.2 Unsupervised Learning 7

1.3.3 Semi-Supervised Learning 7

1.3.4 Reinforcement Learning 8

1.3.5 Deep Learning 8

1.4 Big Data in Healthcare 8

1.5 Application of Big Data in Healthcare 9

1.5.1 Electronic Health Records 9

1.5.2 Helping in Diagnostics 9

1.5.3 Preventive Medicine 10

1.5.4 Precision Medicine 10

1.5.5 Medical Research 10

1.5.6 Cost Reduction 10

1.5.7 Population Health 10

1.5.8 Telemedicine 10

1.5.9 Equipment Maintenance 11

1.5.10 Improved Operational Efficiency 11

1.5.11 Outbreak Prediction 11

1.6 Challenges for Big Data 11

1.7 Conclusion 11

References 12

Part II: Medical Data Processing and Analysis 15

2 Thoracic Image Analysis Using Deep Learning 17
Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi

2.1 Introduction 18

2.2 Broad Overview of Research 19

2.2.1 Challenges 19

2.2.2 Performance Measuring Parameters 21

2.2.3 Availability of Datasets 21

2.3 Existing Models 23

2.4 Comparison of Existing Models 30

2.5 Summary 38

2.6 Conclusion and Future Scope 38

References 39

3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43
G. Manikandan and S. Abirami

3.1 Introduction 43

3.1.1 Motivation of the Dimensionality Reduction 45

3.1.2 Feature Selection and Feature Extraction 46

3.1.3 Objectives of the Feature Selection 47

3.1.4 Feature Selection Process 47

3.2 Types of Feature Selection 48

3.2.1 Filter Methods 49

3.2.1.1 Correlation-Based Feature Selection 49

3.2.1.2 The Fast Correlation-Based Filter 50

3.2.1.3 The INTERACT Algorithm 51

3.2.1.4 ReliefF 51

3.2.1.5 Minimum Redundancy Maximum Relevance 52

3.2.2 Wrapper Methods 52

3.2.3 Embedded Methods 53

3.2.4 Hybrid Methods 54

3.3 Machine Learning and Deep Learning Models 55

3.3.1 Restricted Boltzmann Machine 55

3.3.2 Autoencoder 56

3.3.3 Convolutional Neural Networks 57

3.3.4 Recurrent Neural Network 58

3.4 Real-World Applications and Scenario of Feature Selection 58

3.4.1 Microarray 58

3.4.2 Intrusion Detection 59

3.4.3 Text Categorization 59

3.5 Conclusion 59

References 60

4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65
Parvej Reja Saleh and Eeshankur Saikia

4.1 Introduction 65

4.2 Literature Review 68

4.3 Dataset, EDA, and Data Processing 69

4.4 Machine Learning Algorithms 72

4.4.1 Multinomial Naïve Bayes Classifier 72

4.4.2 Support Vector Machine Classifier 72

4.4.3 Random Forest Classifier 73

4.4.4 K-Nearest Neighbor Classifier 74

4.4.5 Decision Tree Classifier 74

4.4.6 Logistic Regression Classifier 75

4.4.7 Multilayer Perceptron Classifier 76

4.5 Work Architecture 77

4.6 Conclusion 78

References 79

5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features 81
Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar

5.1 Introduction 81

5.1.1 Motivation 82

5.2 Related Work 83

5.3 Theoretical Background 84

5.3.1 Pre-Processing Techniques 84

5.3.2 Spectrogram Generation 85

5.3.2 Feature Extraction 88

5.3.4 Feature Selection 90

5.3.5 Support Vector Machine 91

5.4 Proposed Algorithm 91

5.5 Experimental Results 92

5.5.1 Database 92

5.5.2 Evaluation Metrics 94

5.5.3 Confusion Matrix 94

5.5.4 Results and Discussions 94

5.6 Conclusion 96

References 99

6 Improving Multi-Label Classification in Prototype Selection Scenario 103
Himanshu Suyal and Avtar Singh

6.1 Introduction 103

6.2 Related Work 105

6.3 Methodology 106

6.3.1 Experiments and Evaluation 108

6.4 Performance Evaluation 108

6.5 Experiment Data Set 109

6.6 Experiment Results 110

6.7 Conclusion 117

References 117

7 A Machine Learning-Based Intelligent Computational Framework for the Prediction of Diabetes Disease 121
Maqsood Hayat, Yar Muhammad and Muhammad Tahir

7.1 Introduction 121

7.2 Materials and Methods 123

7.2.1 Dataset 123

7.2.2 Proposed Framework for Diabetes System 124

7.2.3 Pre-Processing of Data 124

7.3 Machine Learning Classification Hypotheses 124

7.3.1 K-Nearest Neighbor 124

7.3.2 Decision Tree 125

7.3.3 Random Forest 126

7.3.4 Logistic Regression 126

7.3.5 Naïve Bayes 126

7.3.6 Support Vector Machine 126

7.3.7 Adaptive Boosting 126

7.3.8 Extra-Tree Classifier 127

7.4 Classifier Validation Method 127

7.4.1 K-Fold Cross-Validation Technique 127

7.5 Performance Evaluation Metrics 127

7.6 Results and Discussion 129

7.6.1 Performance of All Classifiers Using 5-Fold CV Method 129

7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 131

7.6.3 Performance of All Classifiers Using 10-Fold CV Method 133

7.7 Conclusion 137

References 137

8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease 139
Dhilsath Fathima M. and S. Justin Samuel

8.1 Introduction 140

8.2 Related Work 140

8.3 Proposed Method 142

8.3.1 Dataset Description 143

8.3.2 Ensemble Learners for Classification Modeling 144

8.3.2.1 Bagging Ensemble Learners 145

8.3.2.2 Boosting Ensemble Learner 147

8.3.3 Hyperparameter Tuning of Ensemble Learners 151

8.3.3.1 Grid Search Algorithm 151

8.3.3.2 Random Search Algorithm 152

8.4 Experimental Outcomes and Analyses 153

8.4.1 Characteristics of UCI Heart Disease Dataset 153

8.4.2 Experimental Result of Ensemble Learners and Performance Comparison 154

8.4.3 Analysis of Experimental Result 154

8.5 Conclusion 157

References 157

9 Computational Intelligence and Healthcare Informatics Part III--Recent Development and Advanced Methodologies 159
Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj

9.1 Introduction: Simulation in Healthcare 160

9.2 Need for a Healthcare Simulation Process 160

9.3 Types of Healthcare Simulations 161

9.4 AI in Healthcare Simulation 163

9.4.1 Machine Learning Models in Healthcare Simulation 163

9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 163

9.4.2 Deep Learning Models in Healthcare Simulation 169

9.4.2.1 Bi-LSTM-Based Surgical Participant Prediction Model 170

9.5 Conclusion 174

References 174

10 Wolfram's Cellular Automata Model in Health Informatics 179
Sutapa Sarkar and Mousumi Saha

10.1 Introduction 179

10.2 Cellular Automata 181

10.3 Application of Cellular Automata in Health Science 183

10.4 Cellular Automata in Health Informatics 184

10.5 Health Informatics-Deep Learning-Cellular Automata 190

10.6 Conclusion 191

References 191

Part III: Machine Learning and COVID Prospective 193

11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques 195
Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena

11.1 Introduction 195

11.2 Literature Review 196

11.3 Data Pre-Processing 197

11.4 Proposed Methodologies 198

11.4.1 Simple Linear Regression 198

11.4.2 Association Rule Mining 202

11.4.3 Back Propagation Neural Network 203

11.5 Experimental Results 204

11.6 Conclusion and Future Scopes 211

References 212

12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach 215
Sivanantham Kalimuthu

12.1 Introduction 215

12.2 Literature Review 218

12.3 System Design 222

12.3.1 Extracting Feature With WMAR 224

12.4 Result and Discussion 229

12.5 Conclusion 232

References 232

13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network 235
Sayan Das and Jaya Sil

13.1 Introduction 236

13.2 Background Details and Literature Review 239

13.2.1 Fuzzy Set 239

13.2.2 Self-Organizing Mapping 239

13.3 Methodology 240

13.3.1 Severity_Factor of Patient 244

13.3.2 Clustering by Self-Organizing Mapping 249

13.4 Results and Discussion 250

13.5 Conclusion 252

References 252

14 Face Mask Detection in Real-Time Video Stream Using Deep Learning 255
Alok Negi and Krishan Kumar

14.1 Introduction 256

14.2 Related Work 257

14.3 Proposed Work 258

14.3.1 Dataset Description 258

14.3.2 Data Pre-Processing and Augmentation 258

14.3.3 VGG19 Architecture and Implementation 259

14.3.4 Face Mask Detection From Real-Time Video Stream 261

14.4 Results and Evaluation 262

14.5 Conclusion 267

References 267

15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms 269
Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar

15.1 Introduction 270

15.2 Research Problem Statements 274

15.3 Dataset Description 274

15.4 Machine Learning Technique Used for Skin Disease Identification 276

15.4.1 Logistic Regression 277

15.4.1.1 Logistic Regression Assumption 277

15.4.1.2 Logistic Sigmoid Function 277

15.4.1.3 Cost Function and Gradient Descent 278

15.4.2 SVM 279

15.4.3 Recurrent Neural Networks 281

15.4.4 Decision Tree Classification Algorithm 283

15.4.5 CNN 286

15.4.6 Random Forest 288

15.5 Result and Analysis 290

15.6 Conclusion 291

References 291

16 Asymptotic Patients' Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario 297
Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari

16.1 Introduction 298

16.1.1 Motivation 298

16.1.2 Contributions 299

16.1.3 Paper Organization 299

16.1.4 System Model Problem Formulation 299

16.1.5 Proposed Methodology 300

16.2 Material Properties and Design Specifications 301

16.2.1 Hardware Components 301

16.2.1.1 Microcontroller 301

16.2.1.2 ESP8266 Wi-Fi Shield 301

16.2.2 Sensors 301

16.2.2.1 Temperature Sensor (LM 35) 301

16.2.2.2 ECG Sensor (AD8232) 301

16.2.2.3 Pulse Sensor 301

16.2.2.4 GPS Module (NEO 6M V2) 302

16.2.2.5 Gyroscope (GY-521) 302

16.2.3 Software Components 302

16.2.3.1 Arduino Software 302

16.2.3.2 MySQL Database 302

16.2.3.3 Wireless Communication 302

16.3 Experimental Methods and Materials 303

16.3.1 Simulation Environment 303

16.3.1.1 System Hardware 303

16.3.1.2 Connection and Circuitry 304

16.3.1.3 Protocols Used 306

16.3.1.4 Libraries Used 307

16.4 Simulation Results 307

16.5 Conclusion 310

16.6 Abbreviations and Acronyms 310

References 311

17 COVID-19 Detection System Using Cellular Automata-Based Segmentation Techniques 313
Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik

17.1 Introduction 313

17.2 Literature Survey 314

17.2.1 Cellular Automata 315

17.2.2 Image Segmentation 316

17.2.3 Deep Learning Techniques 316

17.3 Proposed Methodology 317

17.4 Results and Discussion 320

17.5 Conclusion 322

References 322

18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures 325
Abhilash C. B. and Kavi Mahesh

18.1 Introduction 326

18.2 Methods 326

18.2.1 Data 326

18.3 GSA Model: Graph-Based Statistical Analysis 327

18.4 Graph-Based Analysis 329

18.4.1 Modeling Your Data as a Graph 329

18.4.2 RDF for Knowledge Graph 331

18.4.3 Knowledge Graph Representation 331

18.4.4 RDF Triple for KaTrace 333

18.4.5 Cipher Query Operation on Knowledge Graph 335

18.4.5.1 Inter-District Travel 335

18.4.5.2 Patient 653 Spread Analysis 336

18.4.5.3 Spread Analysis Using Parent-Child Relationships 337

18.4.5.4 Delhi Congregation Attended the Patient's Analysis 339

18.5 Machine Learning Techniques 339

18.5.1 Apriori Algorithm 339

18.5.2 Decision Tree Classifier 341

18.5.3 System Generated Facts on Pandas 343

18.5.4 Time Series Model 345

18.6 Exploratory Data Analysis 346

18.6.1 Statistical Inference 347

18.7 Conclusion 356

18.8 Limitations 356

Acknowledgments 356

Abbreviations 357

References 357

Part IV: Prospective of Computational Intelligence in Healthcare 359

19 Conceptualizing Tomorrow's Healthcare Through Digitization 361
Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena

19.1 Introduction 361

19.2 Importance of IoMT in Healthcare 362

19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 363

19.3.1 Introduction to the Case Study 363

19.3.2 Merits 363

19.3.3 Proposed Design 363

19.3.3.1 Homecare 363

19.3.3.2 Healthcare Provider 365

19.3.3.3 Community 367

19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 371

19.4.1 Introduction to the Case Study 371

19.4.2 Proposed Design 373

19.5 Future of Smart Healthcare 375

19.6 Conclusion 375

References 375

20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach 377
Pitambar Behera and Om Prakash Jena

20.1 Introduction 377

20.1.1 COVID-19 Pandemic Situation 378

20.1.2 Salient Characteristics of Biomedical Corpus 378

20.2 Review of Related Literature 379

20.2.1 Biomedical NLP Research 379

20.2.2 Domain Adaptation 379

20.2.3 POS Tagging in Hindi 380

20.3 Scope and Objectives 380

20.3.1 Research Questions 380

20.3.2 Research Problem 380

20.3.3 Objectives 381

20.4 Methodological Design 381

20.4.1 Method of Data Collection 381

20.4.2 Method of Data Annotation 381

20.4.2.1 The BIS Tagset 381

20.4.2.2 ILCI Semi-Automated Annotation Tool 382

20.4.2.3 IA Agreement 383

20.4.3 Method of Data Analysis 383

20.4.3.1 The Theory of Support Vector Machines 384

20.4.3.2 Experimental Setup 384

20.5 Evaluation 385

20.5.1 Error Analysis 386

20.5.2 Fleiss' Kappa 388

20.6 Issues 388

20.7 Conclusion and Future Work 388

Acknowledgements 389

References 389

21 Application of Natural Language Processing in Healthcare 393
Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas

21.1 Introduction 393

21.2 Evolution of Natural Language Processing 395

21.3 Outline of NLP in Medical Management 396

21.4 Levels of Natural Language Processing in Healthcare 397

21.5 Opportunities and Challenges From a Clinical Perspective 399

21.5.1 Application of Natural Language Processing in the Field of Medical Health Records 399

21.5.2 Using Natural Language Processing for Large-Sample Clinical Research 400

21.6 Openings and Difficulties From a Natural Language Processing Point of View 401

21.6.1 Methods for Developing Shareable Data 401

21.6.2 Intrinsic Evaluation and Representation Levels 402

21.6.3 Beyond Electronic Health Record Data 403

21.7 Actionable Guidance and Directions for the Future 403

21.8 Conclusion 406

References 406

Index 409
Om Prakash Jena PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. He has more than 30 research articles in peer-reviewed journals and 4 patents.

Alok Ranjan Tripathy PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.

Ahmed A. Elngar PhD is an assistant professor of Computer Science, Chair of Scientific Innovation Research Group (SIRG), Director of Technological and Informatics Studies Center, at Beni-Suef University, Egypt.

Zdzislaw Polkowski PhD is Professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals.

O. P. Jena, Ravenshaw University, India; A. R. Tripathy, Ravenshaw University, India; A. A. Elngar, Beni-Suef University, Egypt; Z. Polkowski, Jan Wyzykowski University, Poland