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.

• Preface xvii1 An Introduction to Knowledge Engineering and Data Analytics 1D. Karthika and K. Kalaiselvi1.1 Introduction 21.1.1 Online Learning and Fragmented Learning Modeling 21.2 Knowledge and Knowledge Engineering 51.2.1 Knowledge 51.2.2 Knowledge Engineering 51.3 Knowledge Engineering as a Modelling Process 61.4 Tools 71.5 What are KBSs? 81.5.1 What is KBE? 81.5.2 When Can KBE Be Used? 101.5.3 CAD or KBE? 121.6 Guided Random Search and Network Techniques 131.6.1 Guide Random Search Techniques 131.7 Genetic Algorithms 141.7.1 Design Point Data Structure 151.7.2 Fitness Function 151.7.3 Constraints 161.7.4 Hybrid Algorithms 161.7.5 Considerations When Using a GA 161.7.6 Alternative to Genetic-Inspired Creation of Children 171.7.7 Alternatives to GA 181.7.8 Closing Remarks for GA 181.8 Artificial Neural Networks 191.9 Conclusion 19References 202 A Framework for Big Data Knowledge Engineering 21Devi T. and Ramachandran A.2.1 Introduction 222.1.1 Knowledge Engineering in AI and Its Techniques 232.1.1.1 Supervised Model 232.1.1.2 Unsupervised Model 232.1.1.3 Deep Learning 242.1.1.4 Deep Reinforcement Learning 242.1.1.5 Optimization 252.1.2 Disaster Management 252.2 Big Data in Knowledge Engineering 262.2.1 Cognitive Tasks for Time Series Sequential Data 272.2.2 Neural Network for Analyzing the Weather Forecasting 272.2.3 Improved Bayesian Hidden Markov Frameworks 282.3 Proposed System 302.4 Results and Discussion 322.5 Conclusion 33References 363 Big Data Knowledge System in Healthcare 39P. Sujatha, K. Mahalakshmi and P. Sripriya3.1 Introduction 403.2 Overview of Big Data 413.2.1 Big Data: Definition 413.2.2 Big Data: Characteristics 423.3 Big Data Tools and Techniques 433.3.1 Big Data Value Chain 433.3.2 Big Data Tools and Techniques 453.4 Big Data Knowledge System in Healthcare 453.4.1 Sources of Medical Big Data 513.4.2 Knowledge in Healthcare 533.4.3 Big Data Knowledge Management Systems in Healthcare 553.4.4 Big Data Analytics in Healthcare 563.5 Big Data Applications in the Healthcare Sector 593.5.1 Real Time Healthcare Monitoring and Altering 593.5.2 Early Disease Prediction with Big Data 593.5.3 Patients Predictions for Improved Staffing 613.5.4 Medical Imaging 613.6 Challenges with Healthcare Big Data 623.6.1 Challenges of Big Data 623.6.2 Challenges of Healthcare Big Data 623.7 Conclusion 64References 644 Big Data for Personalized Healthcare 67Dhanalakshmi R. and Jose Anand4.1 Introduction 684.1.1 Objectives 684.1.2 Motivation 694.1.3 Domain Description 704.1.4 Organization of the Chapter 704.2 Related Literature 714.2.1 Healthcare Cyber Physical System Architecture 714.2.2 Healthcare Cloud Architecture 714.2.3 User Authentication Management 724.2.4 Healthcare as a Service (HaaS) 724.2.5 Reporting Services 734.2.6 Chart and Trend Analysis 734.2.7 Medical Data Analysis 734.2.8 Hospital Platform Based On Cloud Computing 744.2.9 Patient’s Data Collection 744.2.10 H-Cloud Challenges 754.2.11 Healthcare Information System and Cost 754.3 System Analysis and Design 754.3.1 Proposed Solution 764.3.2 Software Components 764.3.3 System Design 764.3.4 Architecture Diagram 774.3.5 List of Modules 784.3.6 Use Case Diagram 814.3.7 Sequence Diagram 814.3.8 Class Diagram 824.4 System Implementation 834.4.1 User Interface 834.4.2 Storage Module 844.4.3 Notification Module 854.4.4 Middleware 864.4.5 OTP Module 874.5 Results and Discussion 884.6 Conclusion 90References 905 Knowledge Engineering for AI in Healthcare 93A. Thirumurthi Raja and B. Mahalakshmi5.1 Introduction 945.2 Overview 955.2.1 Knowledge Representation 955.2.2 Types of Knowledge in Artificial Intelligence 965.2.3 Relation Between Knowledge and Intelligence 975.2.4 Approaches to Knowledge Representation 975.2.5 Requirements for Knowledge Representation System 985.2.6 Techniques of Knowledge Representation 985.2.6.1 Logical Representation 995.2.6.2 Semantic Network Representation 995.2.6.3 Frame Representation 995.2.6.4 Production Rules 1005.2.7 Process of Knowledge Engineering 1015.2.8 Knowledge Discovery Process 1065.3 Applications of Knowledge Engineering in AI for Healthcare 1065.3.1 AI Supports in Clinical Decisions 1075.3.2 AI-Assisted Robotic Surgery 1075.3.3 Enhance Primary Care and Triage 1085.3.4 Clinical Judgments or Diagnosis 1085.3.5 Precision Medicine 1095.3.6 Drug Discovery 1095.3.7 Deep Learning to Diagnose Diseases 1105.3.8 Automating Administrative Tasks 1115.3.9 Reducing Operational Costs 1125.3.10 Virtual Nursing Assistants 1135.4 Conclusion 113References 1146 Business Intelligence and Analytics from Big Data to Healthcare 115Maheswari P., A. Jaya and João Manuel R. S. Tavares6.1 Introduction 1166.1.1 Impact of Healthcare Industry on Economy 1166.1.2 Coronavirus Impact on the Healthcare Industry 1176.1.3 Objective of the Study 1176.1.4 Limitations of the Study 1176.2 Related Works 1186.3 Conceptual Healthcare Stock Prediction System 1206.3.1 Data Source 1226.3.2 Business Intelligence and Analytics Framework 1226.3.2.1 Simple Machine Learning Model 1226.3.2.2 Time Series Forecasting 1236.3.2.3 Complex Deep Neural Network 1236.3.3 Predicting the Stock Price 1246.4 Implementation and Result Discussion 1246.4.1 Apollo Hospitals Enterprise Limited 1256.4.2 Cadila Healthcare Ltd 1256.4.3 Dr. Reddy’s Laboratories 1286.4.4 Fortis Healthcare Limited 1306.4.5 Max Healthcare Institute Limited 1316.4.6 Opto Circuits Limited 1316.4.7 Panacea Biotec 1356.4.8 Poly Medicure Ltd 1366.4.9 Thyrocare Technologies Limited 1386.4.10 Zydus Wellness Ltd 1386.5 Comparisons of Healthcare Stock Prediction Framework 1416.6 Conclusion and Future Enhancement 143References 143Books 145Web Citation 1457 Internet of Things and Big Data Analytics for Smart Healthcare 147Sathish Kumar K., Om Prakash P.G., Alangudi Balaji N. and Robertas Damaševičius7.1 Introduction 1487.2 Literature Survey 1497.3 Smart Healthcare Using Internet of Things and Big Data Analytics 1517.3.1 Smart Diabetes Prediction 1517.3.2 Smart ADHD Prediction 1547.4 Security for Internet of Things 1597.4.1 K(Binary) ECC FSM 1597.4.2 NAF Method 1607.4.3 K-NAF Multiplication Architecture 1617.4.4 K(NAF) ECC FSM 1617.5 Conclusion 164References 1658 Knowledge-Driven and Intelligent Computing in Healthcare 167R. Mervin, Dinesh Mavalaru and Tintu Thomas8.1 Introduction 1688.1.1 Basics of Health Recommendation System 1698.1.2 Basics of Ontology 1698.1.3 Need of Ontology in Health Recommendation System 1708.2 Literature Review 1718.2.1 Ontology in Various Domain 1728.2.2 Ontology in Health Recommendation System 1748.3 Framework for Health Recommendation System 1758.3.1 Domain Ontology Creation 1768.3.2 Query Pre-Processing 1788.3.3 Feature Selection 1798.3.4 Recommendation System 1808.4 Experimental Results 1828.5 Conclusion and Future Perspective 183References 1839 Secure Healthcare Systems Based on Big Data Analytics 189A. Angel Cerli, K. Kalaiselvi and Vijayakumar Varadarajan9.1 Introduction 1909.2 Healthcare Data 1939.2.1 Structured Data 1939.2.2 Unstructured Data 1949.2.3 Semi-Structured Data 1949.2.4 Genomic Data 1949.2.5 Patient Behavior and Sentiment Data 1949.2.6 Clinical Data and Clinical Notes 1949.2.7 Clinical Reference and Health Publication Data 1959.2.8 Administrative and External Data 1959.3 Recent Works in Big Data Analytics in Healthcare Data 1959.4 Healthcare Big Data 1979.5 Privacy of Healthcare Big Data 1989.6 Privacy Right by Country and Organization 2009.7 How Blockchain is Big Data Usable for Healthcare 2009.7.1 Digital Trust 2009.7.2 Smart Data Tracking 2029.7.3 Ecosystem Sensible 2029.7.4 Switch Digital 2029.7.5 Cybersecurity 2039.7.6 Sharing Interoperability and Data 2039.7.7 Improving Research and Development (R&D) 2069.7.8 Drugs Fighting Counterfeit 2069.7.9 Patient Mutual Participation 2069.7.10 Internet Access by Patient to Longitudinal Data 2069.7.11 Data Storage into Off Related to Confidentiality and Data Scale 2079.8 Blockchain Threats and Medical Strategies Big Data Technology 2079.9 Conclusion and Future Research 208References 20810 Predictive and Descriptive Analysis for Healthcare Data 213Pritam R. Ahire and Rohini Hanchate10.1 Introduction 21410.2 Motivation 21510.2.1 Healthcare Analysis 21510.2.2 Predictive Analytics 21710.2.3 Predictive Analytics Current Trends 21710.2.3.1 Importance of PA 21710.2.4 Descriptive Analysis 21810.2.4.1 Descriptive Statistics 21810.2.4.2 Categories of Descriptive Analysis 21910.2.5 Method of Modeling 22110.2.6 Measures of Data Analytics 22110.2.7 Healthcare Data Analytics Platforms and Tools 22310.2.8 Challenges 22510.2.9 Issues in Predictive Healthcare Analysis 22610.2.9.1 Integrating Separate Data Sources 22610.2.9.2 Advanced Cloud Technologies 22610.2.9.3 Privacy and Security 22710.2.9.4 The Fast Pace of Technology Changes 22710.2.10 Applications of Predictive Analysis 22710.2.10.1 Improving Operational Efficiency 22710.2.10.2 Personal Medicine 22810.2.10.3 Population Health and Risk Scoring 22810.2.10.4 Outbreak Prediction 22810.2.10.5 Controlling Patient Deterioration 22810.2.10.6 Supply Chain Management 22810.2.10.7 Potential in Precision Medicine 22910.2.10.8 Cost Savings From Reducing Waste and Fraud 22910.3 Conclusion 229References 22911 Machine and Deep Learning Algorithms for Healthcare Applications 233K. France, A. Jaya and Doru Tiliute11.1 Introduction 23411.2 Artificial Intelligence, Machine Learning, and Deep Learning 23411.3 Machine Learning 23611.3.1 Supervised Learning 23611.3.2 Unsupervised Learning 23811.3.3 Semi-Supervised 23811.3.4 Reinforcement Learning 23811.4 Advantages of Using Deep Learning on Top of Machine Learning 23911.5 Deep Learning Architecture 23911.6 Medical Image Analysis using Deep Learning 24211.7 Deep Learning in Chest X-Ray Images 24311.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval 24611.9 Image Retrieval Performance Metrics 24911.10 Conclusion 250References 25012 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering 255S. Asha, Kanchana Devi V. and G. Sahaja Vaishnavi12.1 Introduction 25612.2 Literature Review 26012.3 AI in Healthcare 26612.4 Data Science and Knowledge Engineering for COVID-19 26812.5 Proposed Architecture and Its Implementation 27012.5.1 Implementation 27012.5.1.1 Data Collection 27012.5.1.2 Understanding Class and Dependencies 27012.5.1.3 Pre-Processing 27212.5.1.4 Sampling 27312.5.1.5 Model Fixing 27312.5.1.6 Analysis of Real-Time Datasets 27312.5.1.7 Machine Learning Algorithms 27612.6 Conclusions and Future Work 278References 28013 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System 285Agasba Saroj S. J., B. Saleena and B. Prakash13.1 Introduction 28513.1.1 Motivation 28713.2 Ongoing Research on Intelligent Decision Support System 28913.3 Methodology and Architecture of the Intelligent Rule-Based System 29113.3.1 Proposed System Design 29213.3.2 Algorithms Used 29313.3.2.1 Forward Chaining 29313.3.2.2 Backward Chaining 29413.4 Creating a Rule-Based System using Prolog 29513.5 Results and Discussions 30413.6 Conclusion 30613.7 Acknowledgments 307References 30714 Big Data in Healthcare: Management, Analysis, and Future Prospects 309A. Akila, R. Parameswari and C. Jayakumari14.1 Introduction 30914.2 Breast Cancer: Overview 31014.3 State-of-the-Art Technology in Treatment of Cancer 31114.3.1 Chemotherapy 31114.3.2 Radiotherapy 31114.4 Early Diagnosis of Breast Cancer: Overview 31214.4.1 Advantages and Risks Associated with the Early Detection of Breast Cancer 31214.4.2 Diagnosis the Breast Cancer 31314.5 Literature Review 31414.6 Machine Learning Algorithms 31514.6.1 Principal Component Analysis Algorithms 31614.6.2 K-Means Algorithm 31714.6.3 K-Nearest Neighbor Algorithm 31714.6.4 Logistic Regression Algorithm 31814.6.5 Support Vector Machine Algorithm 31814.6.6 AdaBoost Algorithm 31914.6.7 Neural Networks Algorithm 31914.6.8 Random Forest Algorithm 31914.7 Result and Discussion 32014.7.1 Performance Metrics 32014.7.1.1 ROC Curve 32014.7.1.2 Accuracy 32114.7.1.3 Precision and Recall 32114.7.1.4 F1-Score 32214.8 Experimental Result and Discussion 32214.9 Conclusion 324References 32515 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System 327G. Jaculine Priya and S. Saradha15.1 Introduction 32715.2 Machine Learning in Healthcare 32915.3 Types of Learnings in Machine Learning 33115.3.1 Supervised Learning 33215.3.2 Unsupervised Algorithms 33315.3.3 Semi-Supervised Learning 33415.3.4 Reinforcement Learning 33415.4 Types of Machine Learning Algorithms 33415.4.1 Classification 33515.4.2 Bayes Classification 33515.4.3 Association Analysis 33515.4.4 Correlation Analysis 33615.4.5 Cluster Analysis 33615.4.6 Outlier Analysis 33615.4.7 Regression Analysis 33715.4.8 K-Means 33715.4.9 Apriori Algorithm 33715.4.10 K Nearest Neighbor 33715.4.11 Naive Bayes 33815.4.12 AdaBoost 33815.4.13 Support Vector Machine 33815.4.14 Classification and Regression Trees 33915.4.15 Linear Discriminant Analysis 33915.4.16 Logistic Regression 33915.4.17 Linear Regression 33915.4.18 Principal Component Analysis 33915.5 Machine Learning for Information Extraction 34015.5.1 Natural Language Processing 34015.6 Predictive Analysis in Healthcare 34115.7 Conclusion 342References 34216 Knowledge Fusion Patterns in Healthcare 345N. Deepa and N. Kanimozhi16.1 Introduction 34616.2 Related Work 34816.3 Materials and Methods 34916.3.1 Classification of Data Fusion 34916.3.2 Levels and Its Working in Healthcare Ecosystems 35116.3.2.1 Initial Level Data Access (ILA) 35116.3.2.2 Middle Level Access (MLA) 35216.3.2.3 High Level Access (HLA) 35216.4 Proposed System 35216.4.1 Objective 35316.4.2 Sample Dataset 35516.5 Results and Discussion 35516.6 Conclusion and Future Work 361References 36217 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 365J.K. Adedeji, T.O. Owolabi and R.S. Fayose17.1 Introduction 36617.2 Materials and Methods 36717.2.1 Data Acquisition and Pre-Processing 36717.2.2 Sickle Cells Normalization Image 36817.2.3 Gradient Calculation 36917.2.4 Gradient Descent Step 37117.2.5 Insight to Previous Methods Adopted in Convolutional Neural Networks 37217.2.6 Segments of Convolutional Neural Networks 37217.2.6.1 Convolutional Layer 37217.2.6.2 Pooling Layer 37317.2.6.3 Fully Connected Layer 37417.2.6.4 Softmax Layer 37417.2.7 Basic Transformations of Convolutional Neural Networks in Healthcare 37417.2.8 Algorithm Review and Comparison 37617.2.9 Feedforward 37617.3 Results and Discussion 37717.3.1 Results on Suitability for Applications in Healthcare 37717.3.2 Class Prediction 37717.3.3 The Model Sanity Checking 37717.3.4 Analysis of the Epoch and Training Losses 37817.3.5 Discussion and Healthcare Interpretations 37917.3.6 Load Data 37917.3.7 Image Pre-Processing 38017.3.8 Building and Training the Classifier 38117.3.9 Saving the Checkpoint Suitable for Healthcare 38217.3.10 Loading the Checkpoint 38317.4 Conclusion 383References 38318 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 387Niha K. and Aisha Banu W.18.1 Introduction 38818.2 Related Work 38918.3 Convolutional Layer 38918.4 Pooling Layer 39018.5 Fully Connected Layer 39018.6 Recurrent Neural Network 39118.7 LSTM and GRU 39218.8 Materials and Methods 39718.8.1 Pre-Processing Strategy Selection 39718.8.2 Feature Extraction and Classification 40018.9 Results and Discussions 40618.10 Conclusion 40818.11 Acknowledgement 409References 409Index 413