Table of Contents

Cover

Title Page

Preface

1 An Introduction to Knowledge Engineering and Data Analytics

1.1 Introduction
1.2 Knowledge and Knowledge Engineering
1.3 Knowledge Engineering as a Modelling Process
1.4 Tools
1.5 What are KBSs?
1.6 Guided Random Search and Network Techniques
1.7 Genetic Algorithms
1.8 Artificial Neural Networks
1.9 Conclusion
References

2 A Framework for Big Data Knowledge Engineering

2.1 Introduction
2.2 Big Data in Knowledge Engineering
2.3 Proposed System
2.4 Results and Discussion
2.5 Conclusion
References

3 Big Data Knowledge System in Healthcare

3.1 Introduction
3.2 Overview of Big Data
3.3 Big Data Tools and Techniques
3.4 Big Data Knowledge System in Healthcare
3.5 Big Data Applications in the Healthcare Sector
3.6 Challenges with Healthcare Big Data
3.7 Conclusion
References

4 Big Data for Personalized Healthcare

4.1 Introduction
4.2 Related Literature
4.3 System Analysis and Design
4.4 System Implementation
4.5 Results and Discussion
4.6 Conclusion
References

5 Knowledge Engineering for AI in Healthcare

5.1 Introduction
5.2 Overview
5.3 Applications of Knowledge Engineering in AI for Healthcare
5.4 Conclusion
References

6 Business Intelligence and Analytics from Big Data to Healthcare

6.1 Introduction
6.2 Related Works
6.3 Conceptual Healthcare Stock Prediction System
6.4 Implementation and Result Discussion
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

7.1 Introduction
7.2 Literature Survey
7.3 Smart Healthcare Using Internet of Things and Big Data Analytics
7.4 Security for Internet of Things
7.5 Conclusion
References

8 Knowledge-Driven and Intelligent Computing in Healthcare

8.1 Introduction
8.2 Literature Review
8.3 Framework for Health Recommendation System
8.4 Experimental Results
8.5 Conclusion and Future Perspective
References

9 Secure Healthcare Systems Based on Big Data Analytics

9.1 Introduction
9.2 Healthcare 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
9.8 Blockchain Threats and Medical Strategies Big Data Technology
9.9 Conclusion and Future Research
References

10 Predictive and Descriptive Analysis for Healthcare Data

10.1 Introduction
10.2 Motivation
10.3 Conclusion
References

11 Machine and Deep Learning Algorithms for Healthcare Applications

11.1 Introduction
11.2 Artificial Intelligence, Machine Learning, and Deep Learning
11.3 Machine 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

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
12.6 Conclusions and Future Work
References

13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System

13.1 Introduction
13.2 Ongoing Research on Intelligent Decision Support System
13.3 Methodology and Architecture of the Intelligent Rule-Based System
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

14.1 Introduction
14.2 Breast Cancer: Overview
14.3 State-of-the-Art Technology in Treatment of Cancer
14.4 Early Diagnosis of Breast Cancer: Overview
14.5 Literature Review
14.6 Machine Learning Algorithms
14.7 Result and Discussion
14.8 Experimental Result and Discussion
14.9 Conclusion
References

15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System

15.1 Introduction
15.2 Machine Learning in Healthcare
15.3 Types of Learnings in Machine Learning
15.4 Types of Machine Learning Algorithms
15.5 Machine Learning for Information Extraction
15.6 Predictive Analysis in Healthcare
15.7 Conclusion
References

16 Knowledge Fusion Patterns in Healthcare

16.1 Introduction
16.2 Related Work
16.3 Materials and Methods
16.4 Proposed System
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

17.1 Introduction
17.2 Materials and Methods
17.3 Results and Discussion
17.4 Conclusion
References

18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare

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
18.9 Results and Discussions
18.10 Conclusion
18.11 Acknowledgement
References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Knowledge engineering.
Figure 1.2 Knowledge as modelling process.
Figure 1.3 KBE.

Chapter 2

Figure 2.1 Traditional Bayesian Neural Network disaster prediction from the data...
Figure 2.2 Proposed system for predicting disaster using improved Bayesian hidde...
Figure 2.3 Total number of disaster analysis using improved Bayesian Markov chai...
Figure 2.4 Changes from various impacts from natural disaster.
Figure 2.5 Economic damage changes a prediction analysis.
Figure 2.6 Boxplot view of natural disaster on various entity.

Chapter 3

Figure 3.1 Dimensions of big data.
Figure 3.2 Big data value creation flow.
Figure 3.3 Different sources of healthcare data.
Figure 3.4 Knowledge discovery process of big data in healthcare.

Chapter 4

Figure 4.1 Architecture diagram.
Figure 4.2 Functional block diagram.
Figure 4.3 Storage block.
Figure 4.4 Reporting block.
Figure 4.5 Analysis block.
Figure 4.6 Management block.
Figure 4.7 Use case diagram.
Figure 4.8 Sequence diagram.
Figure 4.9 Class diagram.
Figure 4.10 Cases of patients.
Figure 4.11 Notifications of medicines to endpoints.
Figure 4.12 Admin dashboard.

Chapter 5

Figure 5.1 Process of knowledge engineering.
Figure 5.2 Data science and knowledge engineering.

Chapter 6

Figure 6.1 Conceptual healthcare stock prediction system.
Figure 6.2 Overview of business intelligence and analytics framework.
Figure 6.3 Illustration of healthcare stock prediction system.
Figure 6.4 Prediction of the closing price using LR.
Figure 6.5 Prediction of the closing price using ARIMA.
Figure 6.6 Prediction of the closing price using LSTM.
Figure 6.7 Prediction of the closing price using LR.
Figure 6.8 Prediction of the closing price using ARIMA.
Figure 6.9 Prediction of the closing price using LSTM.
Figure 6.10 Prediction of the closing price using LR.
Figure 6.11 Prediction of the closing price using ARIMA.
Figure 6.12 Prediction of the closing price using LSTM.
Figure 6.13 Prediction of the closing price using LR.
Figure 6.14 Prediction of the closing price using ARIMA.
Figure 6.15 Prediction of the closing price using LSTM.
Figure 6.16 Prediction of the closing price using LR.
Figure 6.17 Prediction of the closing price using ARIMA.
Figure 6.18 Prediction of the closing price using LSTM.
Figure 6.19 Prediction of the closing price using LR.
Figure 6.20 Prediction of the closing price using ARIMA.
Figure 6.21 Prediction of the closing price using LSTM.
Figure 6.22 Prediction of the closing price using LR.
Figure 6.23 Prediction of the closing price using ARIMA.
Figure 6.24 Prediction of the closing price using LSTM.
Figure 6.25 Prediction of the closing price using LR.
Figure 6.26 Prediction of the closing price using ARIMA.
Figure 6.27 Prediction of the closing price using LSTM.
Figure 6.28 Prediction of the closing price using LR.
Figure 6.29 Prediction of the closing price using ARIMA.
Figure 6.30 Prediction of the closing price using LSTM.
Figure 6.31 Prediction of the closing price using LR.
Figure 6.32 Prediction of the closing price using ARIMA.
Figure 6.33 Prediction of the closing price using LSTM.

Chapter 7

Figure 7.1 Block diagram for smart diabetes prediction.
Figure 7.2 Decision tree diagram for attribute age.
Figure 7.3 Categorized into carbohydrate, protein, and fat.
Figure 7.4 Percentages of each category of persons identified from analyzed valu...
Figure 7.5 Conceptual diagram for prediction of ADHD/LD.
Figure 7.6 Decision tree for classification of learners.
Figure 7.7 Classification of learners.
Figure 7.8 Heart disease using naïve bayes classifier.
Figure 7.9 ECC k(binary) FSM.
Figure 7.10 k-NAF ECC processor.
Figure 7.11 k-NAF FSM.
Figure 7.12 k-NAF ECC FSM.
Figure 7.13 Battery charge level measurement in Java application using system pr...

Chapter 8

Figure 8.1 Framework of health recommendation system.
Figure 8.2 Flowchart of health recommendation system.
Figure 8.3 Personal information ontology.
Figure 8.4 SWRL rule for the HRS.
Figure 8.5 Cases of iris dataset.
Figure 8.6 Cases of liver disorder.

Chapter 9

Figure 9.1 Various large data healthcare stakeholders.
Figure 9.2 Benefits in adopting blockchain healthcare privacy information.
Figure 9.3 Various forms of big data tools for healthcare.
Figure 9.4 Electronic medical record (EMR).
Figure 9.5 Different forms of strategies for security.

Chapter 10

Figure 10.1 Different types of data analytics. (a) Percentage (%). (b) Types wit...
Figure 10.2 Disease categorization by age.
Figure 10.3 Disease categorization by age.
Figure 10.4 Challenges in healthcare.

Chapter 11

Figure 11.1 Schematic representation of computer science subfields.
Figure 11.2 Methods of machine learning algorithms.
Figure 11.3 Neural network architecture.
Figure 11.4 Deep learning architecture with multiple layers.
Figure 11.5 Block diagram of the CBIR system.

Chapter 12

Figure 12.1 Comparative study of number of positive COVID-19 cases in various co...
Figure 12.2 Comparison of number of COVID-19 deaths in various countries.
Figure 12.3 COVID-19 statistics worldwide based on total cases, recovered, death...
Figure 12.4 Architecture of the proposed methodology.
Figure 12.5 Complete flow of the proposed methodology.
Figure 12.6 Statistics of COVID-19 recovered patients (male).
Figure 12.7 Statistics of COVID-19 recovered patients (female).
Figure 12.8 Analysis of real time data collected.
Figure 12.9 Comparison of various machine learning algorithms.

Chapter 13

Figure 13.1 Diabetes survey as per the category.
Figure 13.2 Diabetes survey as per the age range.
Figure 13.3 Architecture diagram of the intelligent system for diabetes.
Figure 13.4 Process flow of proposed intelligent system for diabetes.
Figure 13.5 Facts for type_one_diabetes.
Figure 13.6 Rules for type_one_diabetes.
Figure 13.7 Predicted output for type_one_diabetes.
Figure 13.8 Intelligent system’s complete output for type_one_diabetes.

Chapter 14

Figure 14.1 Prediction of breast cancer using machine learning algorithms using ...
Figure 14.2 Prediction of breast cancer using machine learning algorithms.
Figure 14.3 Mitoses distribution in PCA and K-means algorithm.
Figure 14.4 Mitoses distribution in machine learning algorithms.
Figure 14.5 Performance comparison of various machine learning algorithms.

Chapter 15

Figure 15.1 Healthcare data sources.
Figure 15.2 Process of data handling.
Figure 15.3 Applications of ML.
Figure 15.4 Types of learning in ML.
Figure 15.5 Example for KNN.
Figure 15.6 Categories of hyperplane.
Figure 15.7 Process of predictive analytics.

Chapter 16

Figure 16.1 Data fusion hierarchical framework for big data and IoT devices.
Figure 16.2 Proposed architecture TLCA in healthcare ecosystem.
Figure 16.3 Comparison of features to calculate the prediction of data fusion ac...
Figure 16.4 Data fusion along with sensor fusion using TLCA healthcare system.
Figure 16.5 Comparison of IoT devices count based on data aggregation.
Figure 16.6 Number of procedure based on hierarchical ecosystem vs frequency.
Figure 16.7 Accuracy, precision and recall (%) based on distributed framework.

Chapter 17

Figure 17.1 Normal cell and Abnormal cell as viewed under microscope. (Courtesy ...
Figure 17.2 Neural network architecture.
Figure 17.3 The predicted normal red blood cell.
Figure 17.4 The graphs of training losses against epoch numbers.

Chapter 18

Figure 18.1 Deep learning–based absence seizure detection work flow.
Figure 18.2 First eight segments of single instances after augmentation.
Figure 18.3 Feature extraction process with its parameters.
Figure 18.4 Convolution layer output of absence seizure pattern in time and freq...
Figure 18.5 Working of GRU-SVM.
Figure 18.6 Performance of the classifiers.

List of Tables

Chapter 2

Table 2.1 Entities from weather forecasting dataset.
Table 2.2 Sample dataset for predicting weather forecasting.

Chapter 3

Table 3.1 Dimensions of big data.
Table 3.2 Big data technologies [12, 14, 26].
Table 3.3 Difference between electronic health record and electronic medical rec...
Table 3.4 Summary of different sources of healthcare data [13].
Table 3.5 Patient health checking devices.

Chapter 6

Table 6.1 Comparison of LR, ARIMA, and LSTM of MSE and RMSE.

Chapter 7

Table 7.1 Results of learners.

Chapter 8

Table 8.1 Dataset with cases.

Chapter 9

Table 9.1 Knowledge security laws from numerous countries and organizations.
Table 9.2 Blockchain flexibility concerning threats.

Chapter 11

Table 11.1 Comparison of different DNN architectures.
Table 11.2 Different gaps in CBIR systems [36].

Chapter 12

Table 12.1 Characteristics details of COVID-19 in various organs [2].
Table 12.2 AI methods and big data for health care sector.

Chapter 14

Table 14.1 Performance of the various machine learning algorithms.

Chapter 15

Table 15.1 Classification and prediction of customer data set.
Table 15.2 Clustering and association of customer data set.

Chapter 16

Table 16.1 Classification of healthcare data for accurate multiple data integrit...
Table 16.2 Healthcare sample dataset for preprocessing and data fusion.

Chapter 17

Table 17.1 Performance estimation for accuracy.

Chapter 18

Table 18.1 Seizure detection based pre-processing, input formulation, feature ex...
Table 18.2 Details of normal, abnormal, and absence subject.
Table 18.3 Schematics of convolution layer.
Table 18.4 Schematics of GRU-SVM layers.
Table 18.5 p-value of a classification model.

Handbook of Intelligent Healthcare Analytics

Knowledge Engineering with Big Data Analytics

Edited by

A. Jaya

Department of Computer Application, B.S. Abdur Rahman Crescent Institute of Science, Technology, Chennai, India

K. Kalaiselvi

Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai, India

Dinesh Goyal

Poornima Institute of Engineering & Technology, Jaipur, India

and

Dhiya AL-Jumeily

Faculty of Engineering and Technology, Liverpool John Moores University, UK

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

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Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

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10 9 8 7 6 5 4 3 2 1

Preface

The power of healthcare data analytics is being increasingly used in the industry. With this in mind, we wanted to write a book geared towards those who want to learn more about the techniques used in healthcare analytics for efficient analysis of data. Since data is generally generated in enormous amounts and pumped into data pools, analyzing data patterns can help to ensure a better quality of life for patients. As a result of small amounts of health data from patients suffering from various health issues being collectively pooled, researchers and doctors can find patterns in the statistics, helping them develop new ways of forecasting or diagnosing health issues, and identifying possible ways to improve quality clinical care. Big data analytics supports this research by applying various processes to examine large and varied healthcare data sets. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. This book 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, this book also explores various sources of personalized healthcare data.

For those who are healthcare researchers, this book reveals the innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets. Since machine learning algorithms play a major role in analyzing the volume, veracity and velocity of big data, the scope of this book focuses on various kinds of machine learning algorithms existing in the areas such as supervised, unsupervised, semi-supervised, and reinforcement learning. It guides readers in implementing the Python environment for machine learning in various application domains. Furthermore, predictive analytics in healthcare is explored, which can help to detect early signs of patient deterioration from the ICU to a general ward, identify at-risk patients in their homes to prevent hospital readmissions, and prevent avoidable downtime of medical equipment.

Also explored in the book are a wide variety of machine learning techniques that can be applied to infer intelligence from the data set and the capabilities of an application. The significance of data sets for various applications is also discussed along with sample case studies. Moreover, the challenges presented by the techniques and budding research avenues necessary to see their further advancement are highlighted.

Patient’s healthcare data needs to be protected by organizations in order to prevent data loss through unauthorized access. This data needs to be protected from attacks that can encrypt or destroy data, such as ransomware, as well as those attacks that can modify or corrupt a patient’s data. Security is paramount since a lot of devices are connected through the internet of things and serve many healthcare applications, including supporting smart healthcare systems in the management of various diseases such as diabetes, monitoring heart functions, predicting heart failure, etc. Therefore, this book explores the various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., which create a new burden for providers to maintain compliance with healthcare data security.

In addition to inferring knowledge fusion patterns in healthcare, the book also explores the commercial platforms for healthcare data analytics. The new benefits that healthcare data analytics brings to the table, run analytics and unearth information that could be used in the decision-making of practitioners by providing insights that can be used to make immediate decisions. Also investigated are the new trends and applications of big data analytics for medical science and healthcare. 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.

Editors

Dr. A. Jaya

Dr. K. Kalaiselvi*

Dr. Dinesh Goyal

Prof. Dhiya AL-Jumeily

*Corresponding Editor