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Handbook on Intelligent Healthcare Analytics

Knowledge Engineering with Big Data
Edited by A. Jaya, K. Kalaiselvi, Dinesh Goyal and Dhiya AL-Jumeily
Series: Machine Learning in Biomedical Science and Healthcare Informatics
Copyright: 2022   |   Expected Pub Date:2022/05/30
ISBN: 9781119791799  |  Hardcover  |  
413 pages | 147 illustrations
Price: $225 USD
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One Line Description
The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners.

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.

Description
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:
• 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;
• An exploration of predictive analytics in healthcare;
• Thevariouschallengesforsmarthealthcare,includingprivacy,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.

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Author / Editor Details
A. Jaya, PhD, Professor in the Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, India. She 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.

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

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