Details

Handbook on Intelligent Healthcare Analytics

Knowledge Engineering with Big Data
Machine Learning in Biomedical Science and Healthcare Informatics 1. Aufl.

von: A. Jaya, K. Kalaiselvi, Dinesh Goyal, Dhiya Al-Jumeily
173,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	29.04.2022
ISBN/EAN:	9781119792543
Sprache:	englisch
Anzahl Seiten:	448

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Titelbeschreibung

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: <ul><li>Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;</li> <li>An exploration of predictive analytics in healthcare;</li> <li>The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.</ul></li> Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Inhaltsverzeichnis

Preface xvii 1 An Introduction to Knowledge Engineering and Data Analytics 1 D. Karthika and K. Kalaiselvi 1.1 Introduction 2 1.1.1 Online Learning and Fragmented Learning Modeling 2 1.2 Knowledge and Knowledge Engineering 5 1.2.1 Knowledge 5 1.2.2 Knowledge Engineering 5 1.3 Knowledge Engineering as a Modelling Process 6 1.4 Tools 7 1.5 What are KBSs? 8 1.5.1 What is KBE? 8 1.5.2 When Can KBE Be Used? 10 1.5.3 CAD or KBE? 12 1.6 Guided Random Search and Network Techniques 13 1.6.1 Guide Random Search Techniques 13 1.7 Genetic Algorithms 14 1.7.1 Design Point Data Structure 15 1.7.2 Fitness Function 15 1.7.3 Constraints 16 1.7.4 Hybrid Algorithms 16 1.7.5 Considerations When Using a GA 16 1.7.6 Alternative to Genetic-Inspired Creation of Children 17 1.7.7 Alternatives to GA 18 1.7.8 Closing Remarks for GA 18 1.8 Artificial Neural Networks 19 1.9 Conclusion 19 References 20 2 A Framework for Big Data Knowledge Engineering 21 Devi T. and Ramachandran A. 2.1 Introduction 22 2.1.1 Knowledge Engineering in AI and Its Techniques 23 2.1.1.1 Supervised Model 23 2.1.1.2 Unsupervised Model 23 2.1.1.3 Deep Learning 24 2.1.1.4 Deep Reinforcement Learning 24 2.1.1.5 Optimization 25 2.1.2 Disaster Management 25 2.2 Big Data in Knowledge Engineering 26 2.2.1 Cognitive Tasks for Time Series Sequential Data 27 2.2.2 Neural Network for Analyzing the Weather Forecasting 27 2.2.3 Improved Bayesian Hidden Markov Frameworks 28 2.3 Proposed System 30 2.4 Results and Discussion 32 2.5 Conclusion 33 References 36 3 Big Data Knowledge System in Healthcare 39 P. Sujatha, K. Mahalakshmi and P. Sripriya 3.1 Introduction 40 3.2 Overview of Big Data 41 3.2.1 Big Data: Definition 41 3.2.2 Big Data: Characteristics 42 3.3 Big Data Tools and Techniques 43 3.3.1 Big Data Value Chain 43 3.3.2 Big Data Tools and Techniques 45 3.4 Big Data Knowledge System in Healthcare 45 3.4.1 Sources of Medical Big Data 51 3.4.2 Knowledge in Healthcare 53 3.4.3 Big Data Knowledge Management Systems in Healthcare 55 3.4.4 Big Data Analytics in Healthcare 56 3.5 Big Data Applications in the Healthcare Sector 59 3.5.1 Real Time Healthcare Monitoring and Altering 59 3.5.2 Early Disease Prediction with Big Data 59 3.5.3 Patients Predictions for Improved Staffing 61 3.5.4 Medical Imaging 61 3.6 Challenges with Healthcare Big Data 62 3.6.1 Challenges of Big Data 62 3.6.2 Challenges of Healthcare Big Data 62 3.7 Conclusion 64 References 64 4 Big Data for Personalized Healthcare 67 Dhanalakshmi R. and Jose Anand 4.1 Introduction 68 4.1.1 Objectives 68 4.1.2 Motivation 69 4.1.3 Domain Description 70 4.1.4 Organization of the Chapter 70 4.2 Related Literature 71 4.2.1 Healthcare Cyber Physical System Architecture 71 4.2.2 Healthcare Cloud Architecture 71 4.2.3 User Authentication Management 72 4.2.4 Healthcare as a Service (HaaS) 72 4.2.5 Reporting Services 73 4.2.6 Chart and Trend Analysis 73 4.2.7 Medical Data Analysis 73 4.2.8 Hospital Platform Based On Cloud Computing 74 4.2.9 Patient’s Data Collection 74 4.2.10 H-Cloud Challenges 75 4.2.11 Healthcare Information System and Cost 75 4.3 System Analysis and Design 75 4.3.1 Proposed Solution 76 4.3.2 Software Components 76 4.3.3 System Design 76 4.3.4 Architecture Diagram 77 4.3.5 List of Modules 78 4.3.6 Use Case Diagram 81 4.3.7 Sequence Diagram 81 4.3.8 Class Diagram 82 4.4 System Implementation 83 4.4.1 User Interface 83 4.4.2 Storage Module 84 4.4.3 Notification Module 85 4.4.4 Middleware 86 4.4.5 OTP Module 87 4.5 Results and Discussion 88 4.6 Conclusion 90 References 90 5 Knowledge Engineering for AI in Healthcare 93 A. Thirumurthi Raja and B. Mahalakshmi 5.1 Introduction 94 5.2 Overview 95 5.2.1 Knowledge Representation 95 5.2.2 Types of Knowledge in Artificial Intelligence 96 5.2.3 Relation Between Knowledge and Intelligence 97 5.2.4 Approaches to Knowledge Representation 97 5.2.5 Requirements for Knowledge Representation System 98 5.2.6 Techniques of Knowledge Representation 98 5.2.6.1 Logical Representation 99 5.2.6.2 Semantic Network Representation 99 5.2.6.3 Frame Representation 99 5.2.6.4 Production Rules 100 5.2.7 Process of Knowledge Engineering 101 5.2.8 Knowledge Discovery Process 106 5.3 Applications of Knowledge Engineering in AI for Healthcare 106 5.3.1 AI Supports in Clinical Decisions 107 5.3.2 AI-Assisted Robotic Surgery 107 5.3.3 Enhance Primary Care and Triage 108 5.3.4 Clinical Judgments or Diagnosis 108 5.3.5 Precision Medicine 109 5.3.6 Drug Discovery 109 5.3.7 Deep Learning to Diagnose Diseases 110 5.3.8 Automating Administrative Tasks 111 5.3.9 Reducing Operational Costs 112 5.3.10 Virtual Nursing Assistants 113 5.4 Conclusion 113 References 114 6 Business Intelligence and Analytics from Big Data to Healthcare 115 Maheswari P., A. Jaya and João Manuel R. S. Tavares 6.1 Introduction 116 6.1.1 Impact of Healthcare Industry on Economy 116 6.1.2 Coronavirus Impact on the Healthcare Industry 117 6.1.3 Objective of the Study 117 6.1.4 Limitations of the Study 117 6.2 Related Works 118 6.3 Conceptual Healthcare Stock Prediction System 120 6.3.1 Data Source 122 6.3.2 Business Intelligence and Analytics Framework 122 6.3.2.1 Simple Machine Learning Model 122 6.3.2.2 Time Series Forecasting 123 6.3.2.3 Complex Deep Neural Network 123 6.3.3 Predicting the Stock Price 124 6.4 Implementation and Result Discussion 124 6.4.1 Apollo Hospitals Enterprise Limited 125 6.4.2 Cadila Healthcare Ltd 125 6.4.3 Dr. Reddy’s Laboratories 128 6.4.4 Fortis Healthcare Limited 130 6.4.5 Max Healthcare Institute Limited 131 6.4.6 Opto Circuits Limited 131 6.4.7 Panacea Biotec 135 6.4.8 Poly Medicure Ltd 136 6.4.9 Thyrocare Technologies Limited 138 6.4.10 Zydus Wellness Ltd 138 6.5 Comparisons of Healthcare Stock Prediction Framework 141 6.6 Conclusion and Future Enhancement 143 References 143 Books 145 Web Citation 145 7 Internet of Things and Big Data Analytics for Smart Healthcare 147 Sathish Kumar K., Om Prakash P.G., Alangudi Balaji N. and Robertas Damaševičius 7.1 Introduction 148 7.2 Literature Survey 149 7.3 Smart Healthcare Using Internet of Things and Big Data Analytics 151 7.3.1 Smart Diabetes Prediction 151 7.3.2 Smart ADHD Prediction 154 7.4 Security for Internet of Things 159 7.4.1 K(Binary) ECC FSM 159 7.4.2 NAF Method 160 7.4.3 K-NAF Multiplication Architecture 161 7.4.4 K(NAF) ECC FSM 161 7.5 Conclusion 164 References 165 8 Knowledge-Driven and Intelligent Computing in Healthcare 167 R. Mervin, Dinesh Mavalaru and Tintu Thomas 8.1 Introduction 168 8.1.1 Basics of Health Recommendation System 169 8.1.2 Basics of Ontology 169 8.1.3 Need of Ontology in Health Recommendation System 170 8.2 Literature Review 171 8.2.1 Ontology in Various Domain 172 8.2.2 Ontology in Health Recommendation System 174 8.3 Framework for Health Recommendation System 175 8.3.1 Domain Ontology Creation 176 8.3.2 Query Pre-Processing 178 8.3.3 Feature Selection 179 8.3.4 Recommendation System 180 8.4 Experimental Results 182 8.5 Conclusion and Future Perspective 183 References 183 9 Secure Healthcare Systems Based on Big Data Analytics 189 A. Angel Cerli, K. Kalaiselvi and Vijayakumar Varadarajan 9.1 Introduction 190 9.2 Healthcare Data 193 9.2.1 Structured Data 193 9.2.2 Unstructured Data 194 9.2.3 Semi-Structured Data 194 9.2.4 Genomic Data 194 9.2.5 Patient Behavior and Sentiment Data 194 9.2.6 Clinical Data and Clinical Notes 194 9.2.7 Clinical Reference and Health Publication Data 195 9.2.8 Administrative and External Data 195 9.3 Recent Works in Big Data Analytics in Healthcare Data 195 9.4 Healthcare Big Data 197 9.5 Privacy of Healthcare Big Data 198 9.6 Privacy Right by Country and Organization 200 9.7 How Blockchain is Big Data Usable for Healthcare 200 9.7.1 Digital Trust 200 9.7.2 Smart Data Tracking 202 9.7.3 Ecosystem Sensible 202 9.7.4 Switch Digital 202 9.7.5 Cybersecurity 203 9.7.6 Sharing Interoperability and Data 203 9.7.7 Improving Research and Development (R&D) 206 9.7.8 Drugs Fighting Counterfeit 206 9.7.9 Patient Mutual Participation 206 9.7.10 Internet Access by Patient to Longitudinal Data 206 9.7.11 Data Storage into Off Related to Confidentiality and Data Scale 207 9.8 Blockchain Threats and Medical Strategies Big Data Technology 207 9.9 Conclusion and Future Research 208 References 208 10 Predictive and Descriptive Analysis for Healthcare Data 213 Pritam R. Ahire and Rohini Hanchate 10.1 Introduction 214 10.2 Motivation 215 10.2.1 Healthcare Analysis 215 10.2.2 Predictive Analytics 217 10.2.3 Predictive Analytics Current Trends 217 10.2.3.1 Importance of PA 217 10.2.4 Descriptive Analysis 218 10.2.4.1 Descriptive Statistics 218 10.2.4.2 Categories of Descriptive Analysis 219 10.2.5 Method of Modeling 221 10.2.6 Measures of Data Analytics 221 10.2.7 Healthcare Data Analytics Platforms and Tools 223 10.2.8 Challenges 225 10.2.9 Issues in Predictive Healthcare Analysis 226 10.2.9.1 Integrating Separate Data Sources 226 10.2.9.2 Advanced Cloud Technologies 226 10.2.9.3 Privacy and Security 227 10.2.9.4 The Fast Pace of Technology Changes 227 10.2.10 Applications of Predictive Analysis 227 10.2.10.1 Improving Operational Efficiency 227 10.2.10.2 Personal Medicine 228 10.2.10.3 Population Health and Risk Scoring 228 10.2.10.4 Outbreak Prediction 228 10.2.10.5 Controlling Patient Deterioration 228 10.2.10.6 Supply Chain Management 228 10.2.10.7 Potential in Precision Medicine 229 10.2.10.8 Cost Savings From Reducing Waste and Fraud 229 10.3 Conclusion 229 References 229 11 Machine and Deep Learning Algorithms for Healthcare Applications 233 K. France, A. Jaya and Doru Tiliute 11.1 Introduction 234 11.2 Artificial Intelligence, Machine Learning, and Deep Learning 234 11.3 Machine Learning 236 11.3.1 Supervised Learning 236 11.3.2 Unsupervised Learning 238 11.3.3 Semi-Supervised 238 11.3.4 Reinforcement Learning 238 11.4 Advantages of Using Deep Learning on Top of Machine Learning 239 11.5 Deep Learning Architecture 239 11.6 Medical Image Analysis using Deep Learning 242 11.7 Deep Learning in Chest X-Ray Images 243 11.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval 246 11.9 Image Retrieval Performance Metrics 249 11.10 Conclusion 250 References 250 12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering 255 S. Asha, Kanchana Devi V. and G. Sahaja Vaishnavi 12.1 Introduction 256 12.2 Literature Review 260 12.3 AI in Healthcare 266 12.4 Data Science and Knowledge Engineering for COVID-19 268 12.5 Proposed Architecture and Its Implementation 270 12.5.1 Implementation 270 12.5.1.1 Data Collection 270 12.5.1.2 Understanding Class and Dependencies 270 12.5.1.3 Pre-Processing 272 12.5.1.4 Sampling 273 12.5.1.5 Model Fixing 273 12.5.1.6 Analysis of Real-Time Datasets 273 12.5.1.7 Machine Learning Algorithms 276 12.6 Conclusions and Future Work 278 References 280 13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System 285 Agasba Saroj S. J., B. Saleena and B. Prakash 13.1 Introduction 285 13.1.1 Motivation 287 13.2 Ongoing Research on Intelligent Decision Support System 289 13.3 Methodology and Architecture of the Intelligent Rule-Based System 291 13.3.1 Proposed System Design 292 13.3.2 Algorithms Used 293 13.3.2.1 Forward Chaining 293 13.3.2.2 Backward Chaining 294 13.4 Creating a Rule-Based System using Prolog 295 13.5 Results and Discussions 304 13.6 Conclusion 306 13.7 Acknowledgments 307 References 307 14 Big Data in Healthcare: Management, Analysis, and Future Prospects 309 A. Akila, R. Parameswari and C. Jayakumari 14.1 Introduction 309 14.2 Breast Cancer: Overview 310 14.3 State-of-the-Art Technology in Treatment of Cancer 311 14.3.1 Chemotherapy 311 14.3.2 Radiotherapy 311 14.4 Early Diagnosis of Breast Cancer: Overview 312 14.4.1 Advantages and Risks Associated with the Early Detection of Breast Cancer 312 14.4.2 Diagnosis the Breast Cancer 313 14.5 Literature Review 314 14.6 Machine Learning Algorithms 315 14.6.1 Principal Component Analysis Algorithms 316 14.6.2 K-Means Algorithm 317 14.6.3 K-Nearest Neighbor Algorithm 317 14.6.4 Logistic Regression Algorithm 318 14.6.5 Support Vector Machine Algorithm 318 14.6.6 AdaBoost Algorithm 319 14.6.7 Neural Networks Algorithm 319 14.6.8 Random Forest Algorithm 319 14.7 Result and Discussion 320 14.7.1 Performance Metrics 320 14.7.1.1 ROC Curve 320 14.7.1.2 Accuracy 321 14.7.1.3 Precision and Recall 321 14.7.1.4 F1-Score 322 14.8 Experimental Result and Discussion 322 14.9 Conclusion 324 References 325 15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System 327 G. Jaculine Priya and S. Saradha 15.1 Introduction 327 15.2 Machine Learning in Healthcare 329 15.3 Types of Learnings in Machine Learning 331 15.3.1 Supervised Learning 332 15.3.2 Unsupervised Algorithms 333 15.3.3 Semi-Supervised Learning 334 15.3.4 Reinforcement Learning 334 15.4 Types of Machine Learning Algorithms 334 15.4.1 Classification 335 15.4.2 Bayes Classification 335 15.4.3 Association Analysis 335 15.4.4 Correlation Analysis 336 15.4.5 Cluster Analysis 336 15.4.6 Outlier Analysis 336 15.4.7 Regression Analysis 337 15.4.8 K-Means 337 15.4.9 Apriori Algorithm 337 15.4.10 K Nearest Neighbor 337 15.4.11 Naive Bayes 338 15.4.12 AdaBoost 338 15.4.13 Support Vector Machine 338 15.4.14 Classification and Regression Trees 339 15.4.15 Linear Discriminant Analysis 339 15.4.16 Logistic Regression 339 15.4.17 Linear Regression 339 15.4.18 Principal Component Analysis 339 15.5 Machine Learning for Information Extraction 340 15.5.1 Natural Language Processing 340 15.6 Predictive Analysis in Healthcare 341 15.7 Conclusion 342 References 342 16 Knowledge Fusion Patterns in Healthcare 345 N. Deepa and N. Kanimozhi 16.1 Introduction 346 16.2 Related Work 348 16.3 Materials and Methods 349 16.3.1 Classification of Data Fusion 349 16.3.2 Levels and Its Working in Healthcare Ecosystems 351 16.3.2.1 Initial Level Data Access (ILA) 351 16.3.2.2 Middle Level Access (MLA) 352 16.3.2.3 High Level Access (HLA) 352 16.4 Proposed System 352 16.4.1 Objective 353 16.4.2 Sample Dataset 355 16.5 Results and Discussion 355 16.6 Conclusion and Future Work 361 References 362 17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 365 J.K. Adedeji, T.O. Owolabi and R.S. Fayose 17.1 Introduction 366 17.2 Materials and Methods 367 17.2.1 Data Acquisition and Pre-Processing 367 17.2.2 Sickle Cells Normalization Image 368 17.2.3 Gradient Calculation 369 17.2.4 Gradient Descent Step 371 17.2.5 Insight to Previous Methods Adopted in Convolutional Neural Networks 372 17.2.6 Segments of Convolutional Neural Networks 372 17.2.6.1 Convolutional Layer 372 17.2.6.2 Pooling Layer 373 17.2.6.3 Fully Connected Layer 374 17.2.6.4 Softmax Layer 374 17.2.7 Basic Transformations of Convolutional Neural Networks in Healthcare 374 17.2.8 Algorithm Review and Comparison 376 17.2.9 Feedforward 376 17.3 Results and Discussion 377 17.3.1 Results on Suitability for Applications in Healthcare 377 17.3.2 Class Prediction 377 17.3.3 The Model Sanity Checking 377 17.3.4 Analysis of the Epoch and Training Losses 378 17.3.5 Discussion and Healthcare Interpretations 379 17.3.6 Load Data 379 17.3.7 Image Pre-Processing 380 17.3.8 Building and Training the Classifier 381 17.3.9 Saving the Checkpoint Suitable for Healthcare 382 17.3.10 Loading the Checkpoint 383 17.4 Conclusion 383 References 383 18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 387 Niha K. and Aisha Banu W. 18.1 Introduction 388 18.2 Related Work 389 18.3 Convolutional Layer 389 18.4 Pooling Layer 390 18.5 Fully Connected Layer 390 18.6 Recurrent Neural Network 391 18.7 LSTM and GRU 392 18.8 Materials and Methods 397 18.8.1 Pre-Processing Strategy Selection 397 18.8.2 Feature Extraction and Classification 400 18.9 Results and Discussions 406 18.10 Conclusion 408 18.11 Acknowledgement 409 References 409 Index 413

Autorenportrait

A. Jaya, PhD, Professor in the Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, India. She has published more than 90 research articles in international journals. K. Kalaiselvi, PhD, is a Professor and Head in the Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India. She has published more than 50 research articles in international journals. Dinesh Goyal, PhD, is Principal at the Poornima Institute of Engineering & Technology, Jaipur, India. He has six patents published as well as six books and numerous articles. Dhiya Al-Jumeily, PhD, is a professor of Artificial Intelligence and the Associate Dean of External Engagement for the Faculty of Engineering and Technology, Liverpool John Moores University, UK. He has published well over 200 peer-reviewed scientific publications, six books, and five book chapters. His current research is on decision support systems for self-management of health and disease.

Back cover copy

The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: <ul><li>Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;</li> <li>An exploration of predictive analytics in healthcare;</li> <li>The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.</ul></li> Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.