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E-grāmata: Big Data Analytics for Intelligent Healthcare Management

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Edited by , Edited by (Associate Professor, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India; Visiting Fellow , University of Reading, UK), Edited by , Edited by (Assistant Professor, Kalinga Institute of Industrial Technology Universi)
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Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

  • Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more
  • Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc.
  • Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more
Contributors xiii
Preface xvii
Acknowledgments xix
Chapter 1 Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges 1(18)
Sukhpal Singh Gill
Rajkumar Buyya
1.1 Introduction
1(1)
1.1.1 Dimensions of Data Management
2(1)
1.2 Big Data Analytical Model
2(2)
1.3 Bio-Inspired Algorithms for Big Data Analytics: A Taxonomy
4(6)
1.3.1 Evolutionary Algorithms
4(2)
1.3.2 Swarm-Based Algorithms
6(1)
1.3.3 Ecological Algorithms
7(3)
1.3.4 Discussions
10(1)
1.4 Future Research Directions and Open Challenges
10(4)
1.4.1 Resource Scheduling and Usability
10(3)
1.4.2 Data Processing and Elasticity
13(1)
1.4.3 Resilience and Heterogeneity in Interconnected Clouds
13(1)
1.4.4 Sustainability and Energy-Efficiency
13(1)
1.4.5 Data Security and Privacy Protection
13(1)
1.4.6 IoT-Based Edge Computing and Networking
13(1)
1.5 Emerging Research Areas in Bio-Inspired Algorithm-Based Big Data Analytics
14(1)
1.5.1 Container as a Service (CaaS)
14(1)
1.5.2 Serverless Computing as a Service (SCaaS)
14(1)
1.5.3 Blockchain as a Service (BaaS)
14(1)
1.5.4 Software-defined Cloud as a Service (SCaaS)
14(1)
1.5.5 Deep Learning as a Service (DLaaS)
14(1)
1.5.6 Bitcoin as a Service (BiaaS)
15(1)
1.5.7 Quantum Computing as a Service (QCaaS)
15(1)
1.6 Summary and Conclusions
15(1)
Glossary
15(1)
Acknowledgments
15(1)
References
16(1)
Further Reading
17(2)
Chapter 2 Big Data Analytics Challenges and Solutions 19(24)
Ramgopal Kashyap
2.1 Introduction
19(4)
2.1.1 Consumable Massive Facts Analytics
19(2)
2.1.2 Allotted Records Mining Algorithms
21(1)
2.1.3 Gadget Failure
21(1)
2.1.4 Facts Aggregation Challenges
21(1)
2.1.5 Statistics Preservation-Demanding Situations
22(1)
2.1.6 Information Integration Challenges
22(1)
2.2 Records Analysis Challenges
23(2)
2.2.1 Scale of the Statistics
24(1)
2.2.2 Pattern Interpretation Challenges
24(1)
2.3 Arrangements of Challenges
25(1)
2.3.1 User Intervention Method
25(1)
2.3.2 Probabilistic Method
25(1)
2.3.3 Defining and Detecting Anomalies in Human Ecosystems
26(1)
2.4 Demanding Situations in Managing Huge Records
26(1)
2.5 Massive Facts Equal Large Possibilities
27(9)
2.5.1 Present Answers to Challenges for the Quantity Mission
28(1)
2.5.2 Image Mining and Processing With Big Data
29(2)
2.5.3 Potential Answers for Velocity Trouble
31(2)
2.5.4 Ability Solutions for Scalability Assignments
33(3)
2.6 Discussion
36(1)
2.7 Conclusion
37(1)
Glossary
38(1)
References
39(2)
Further Reading
41(2)
Chapter 3 Big Data Analytics in Healthcare: A Critical Analysis 43(16)
Dibya Jyoti Bora
3.1 Introduction
43(1)
3.2 Big Data
44(1)
3.3 Healthcare Data
45(1)
3.3.1 Structured Data
45(1)
3.3.2 Unstructured Data
45(1)
3.3.3 Semistructured Data
45(1)
3.3.4 Genomic Data
45(1)
3.3.5 Patient Behavior and Sentiment Data
45(1)
3.3.6 Clinical Data and Clinical Notes
45(1)
3.3.7 Clinical Reference and Health Publication Data
46(1)
3.3.8 Administrative and External Data
46(1)
3.4 Medical Image Processing and its Role in Healthcare Data Analysis
46(2)
3.5 Recent Works in Big Data Analytics in Healthcare Data
48(3)
3.6 Architectural Framework and Different Tools for Big Data Analytics in Healthcare Big Data
51(3)
3.6.1 Architectural Framework
51(1)
3.6.2 Different Tools Used in Big Data Analytics in Healthcare Data
52(2)
3.7 Challenges Faced During Big Data Analytics in Healthcare
54(1)
3.8 Conclusion and Future Research
55(1)
References
55(2)
Further Reading
57(2)
Chapter 4 Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer 59(28)
Md. Nuruddin Qaisar Bhuiyan
Md. Shamsujjoha
Shamim H. Ripon
Farhin Haque Proma
Fuad Khan
4.1 Introduction
59(1)
4.2 Related Work
60(1)
4.3 Dataset and Methodologies
60(4)
4.3.1 Convolution Neural Networks (CNNs/ConvNets)
60(4)
4.4 Proposed Model
64(2)
4.5 Implementation
66(1)
4.5.1 Feature Extraction
66(1)
4.5.2 Dimensionality Reduction
66(1)
4.5.3 Classification
67(1)
4.5.4 Tuning Hyperparameters of the Classifiers
67(1)
4.6 Result and Analysis
67(16)
4.6.1 10-fold Cross Validation Result
67(1)
4.6.2 Magnification Factor Wise Analysis on Validation Accuracy
67(4)
4.6.3 Result and Analysis of Test Performance
71(12)
4.7 Discussion
83(1)
4.8 Conclusion
83(1)
References
84(2)
Further Reading
86(1)
Chapter 5 Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT 87(64)
Rohit Rastogi
D.K. Chaturvedi
Santosh Satya
Navneet Arora
Mayank Gupta
Vishwas Yadav
Sumit Chauhan
Pallavi Sharma
5.1 Introduction and Background
87(7)
5.1.1 Biofeedback
87(2)
5.1.2 Mental Health Introduction
89(1)
5.1.3 Importance of Mental Health, Stress, and Emotional Needs and Significance of Study
89(1)
5.1.4 Meaning of Mental Health
90(1)
5.1.5 Definitions
90(1)
5.1.6 Factors Affecting Mental Health
91(1)
5.1.7 Models of Stress: Three Models in Practice
91(2)
5.1.8 Big Data and IoT
93(1)
5.2 Previous Studies (Literature Review)
94(1)
5.2.1 Tension Type Headache and Stress
94(1)
5.3 Independent Variable: Emotional Need Fulfillment
95(1)
5.4 Meditation-Effective Spiritual Tool With Approach of Biofeedback EEG
95(1)
5.4.1 Mind-Body and Consciousness
95(1)
5.5 Sensor Modalities and Our Approach
96(2)
5.5.1 Biofeedback Based Sensor Modalities
96(1)
5.5.2 Electromyograph
97(1)
5.5.3 Electrodermograph
97(1)
5.5.4 Proposed Framework
98(1)
5.6 Experiments and Results-Study Plot
98(4)
5.6.1 Study Design and Source of Data
98(1)
5.6.2 Study Duration and Consent From Subjects
98(1)
5.6.3 Sampling Design and Allocation Process
98(1)
5.6.4 Sample Size
98(1)
5.6.5 Study Population
99(1)
5.6.6 Intervention
99(1)
5.6.7 Outcome Parameters
100(1)
5.6.8 Analgesic Consumption
101(1)
5.6.9 Assessment of Outcome Variables
101(1)
5.6.10 Pain Diary
101(1)
5.6.11 Data Collection
101(1)
5.6.12 Statistical Analysis
102(1)
5.6.13 Hypothesis
102(1)
5.7 Data Collection Procedure-Guided Meditation as per Fig. 5.7G
102(1)
5.8 Results, Interpretation and Discussion
102(43)
5.8.1 The Trend of Average of Frequency
108(1)
5.8.2 The Trend of Average of Duration
109(1)
5.8.3 The Trend of Average of Intensity
110(1)
5.8.4 The Trend of Duration per Cycle With Time
111(1)
5.8.5 Trend on Correlation of TTH Duration and Intensity
112(3)
5.8.6 Trend on Correlation of TTH Duration With Occurrence
115(1)
5.8.7 The Trend of Average of Frequency
116(2)
5.8.8 The Trend of Average of Duration
118(1)
5.8.9 The Trend of Average of Intensity
119(1)
5.8.10 The Trend of Duration per Cycle With Time
120(1)
5.8.11 Trend on Correlation of TTH Duration and Intensity
121(2)
5.8.12 Trend on Correlation of TTH Duration With Occurrence
123(3)
5.8.13 The Trend of Average of Frequency
126(1)
5.8.14 The Trend of Average Duration
127(1)
5.8.15 The Trend of Average Intensity
128(1)
5.8.16 The Trend of Duration per Cycle With Time
129(1)
5.8.17 Trend on Correlation of TTH Duration and Intensity
129(3)
5.8.18 Trend on Correlation of TTH Duration With Occurrence
132(4)
5.8.19 The Trend of Average of Frequency
136(1)
5.8.20 The Trend of Average of Duration
137(1)
5.8.21 The Trend of Average Intensity
138(1)
5.8.22 The Trend of Duration per Cycle With Time
139(1)
5.8.23 Trend on Correlation of TTH Duration and Intensity
140(2)
5.8.24 Trend on Correlation of TTH Duration With Occurrence
142(3)
5.9 Findings in This
Chapter
145(1)
5.10 Future Scope, Limitations, and Possible Applications
146(1)
5.11 Conclusion
146(1)
5.11.1 Comprehensive Conclusion
147(1)
Acknowledgment
147(1)
References
147(2)
Further Reading
149(2)
Chapter 6 Multilevel Classification Framework of fMRI Data: A Big Data Approach 151(24)
Luina Pani
Somnath Karmakar
Chinmaya Misra
Satya Ranjan Dash
6.1 Introduction
151(3)
6.2 Related Work
154(3)
6.3 Our Approach
157(9)
6.3.1 Dataset
157(1)
6.3.2 Methodology
157(1)
6.3.3 Result Evaluation
158(1)
6.3.4 Experimental Results
159(1)
6.3.5 Subject-Dependent Experiments on PS +SP
160(3)
6.3.6 Subject-Dependent Experiment on PS/SP
163(3)
6.4 Result Analysis
166(5)
6.4.1 Summary of the Subject-Dependent Results
166(1)
6.4.2 Subject-Independent Experiment
166(5)
6.5 Conclusion and Future Work
171(1)
References
172(2)
Further Reading
174(1)
Chapter 7 Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT 175(22)
Subasish Mohapatra
Suchismita Mohanty
Subhadarshini Mohanty
7.1 Introduction
175(1)
7.2 Literature Survey
176(3)
7.3 Proposed Model
179(4)
7.3.1 Fetch Module
179(1)
7.3.2 Ingest Module
180(1)
7.3.3 Retrieve Module
181(1)
7.3.4 Act/Notify Module
181(2)
7.3.5 Prototype Model of the Proposed Work
183(1)
7.4 Implementation of the Proposed System
183(6)
7.5 Simulation and Result Discussion
189(5)
7.6 Conclusion
194(1)
References
195(2)
Chapter 8 Blockchain in Healthcare: Challenges and Solutions 197(30)
Md. Mehedi Hassan Onik
Satyabrata Aich
Jinhong Yang
Chul-Soo Kim
Hee-Cheol Kim
8.1 Introduction
197(3)
8.1.1 Roadmap
200(1)
8.2 Healthcare Big Data and Blockchain Overview
200(6)
8.2.1 Healthcare Big Data
200(2)
8.2.2 Blockchain
202(4)
8.2.3 How Blockchain Works
206(1)
8.3 Privacy of Healthcare Big Data
206(4)
8.3.1 Privacy Right by Country and Organization
210(1)
8.4 How Blockchain Is Applicable for Healthcare Big Data
210(7)
8.4.1 Digital Trust
210(2)
8.4.2 Intelligent Data Management
212(1)
8.4.3 Smart Ecosystem
212(1)
8.4.4 Digital Supply Chain
213(1)
8.4.5 Cybersecurity
213(1)
8.4.6 Interoperability and Data Sharing
214(1)
8.4.7 Improving Research and Development (R&D)
215(1)
8.4.8 Fighting Counterfeit Drugs
216(1)
8.4.9 Collaborative Patient Engagement
216(1)
8.4.10 Online Access to Longitudinal Data by Patient
217(1)
8.4.11 Off-Chain Data Storage due to Privacy and Data Size
217(1)
8.5 Blockchain Challenges and Solutions for Healthcare Big Data
217(5)
8.5.1 GDPR versus Blockchain
218(4)
8.6 Conclusion and Discussion
222(1)
References
222(4)
Further Reading
226(1)
Chapter 9 Intelligence-Based Health Recommendation System Using Big Data Analytics 227(20)
Abhaya Kumar Sahoo
Sitikantha Mallik
Chittaranjan Pradhan
Bhabani Shankar Prasad Mishra
Rabindra Kumar Barik
Himansu Das
9.1 Introduction
227(1)
9.2 Background
228(7)
9.2.1 Recommendation System and its Basic Concepts
228(1)
9.2.2 Phases of Recommendation System
228(1)
9.2.3 Methodology
229(6)
9.3 Health Recommendation System
235(6)
9.3.1 Designing the Health Recommendation System
236(1)
9.3.2 Framework for HRS
237(2)
9.3.3 Methods to Design HRS
239(1)
9.3.4 Evaluation of HRS
240(1)
9.4 Proposed Intelligent-Based HRS
241(2)
9.4.1 Dataset Description
242(1)
9.4.2 Experimental Result Analysis
242(1)
9.5 Advantages and Disadvantages of the Proposed Health Recommendation System Using Big Data Analytics
243(1)
9.6 Conclusion and Future Work
244(1)
References
245(1)
Further Reading
246(1)
Chapter 10 Computational Biology Approach in Management of Big Data of Healthcare Sector 247(22)
Satya Narayan Sahu
Maheswata Moharana
Sushma Rani Martha
Akalabya Bissoyi
Pradeep Kumar Maharana
Subrat Kumar Pattanayak
10.1 Introduction
247(1)
10.2 Application of Big Data Analysis
248(1)
10.3 Database Management System and Next Generation Sequencing (NGS)
249(1)
10.4 De novo Assembly, Re-Sequencing, Transcriptomics Sequencing and Epigenetics
250(3)
10.5 Data Collection, Extraction of Genes, and Screening of Drugs
253(3)
10.6 Different Algorithms Related to Docking
256(1)
10.7 Molecular Interactions, Scoring Functions, and Discussion of Some Decking Examples
257(6)
10.8 Conclusions
263(1)
Acknowledgments
264(1)
References
264(5)
Chapter 11 Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis 269(14)
Janmenjoy Nayak
Kanithi Vakula
Pandit Byomakesha Dash
Bighnaraj Naik
11.1 Introduction
269(2)
11.2 Biological Structure of the Kidney
271(1)
11.3 Kidney-Inspired Algorithm
271(1)
11.4 Literature Survey
272(2)
11.5 Proposed Model
274(3)
11.5.1 Fuzzy C-Means Algorithm
274(1)
11.5.2 Proposed KA-Based Approach for Biomedical Data Analysis
274(3)
11.6 Results Analysis
277(2)
11.6.1 Evaluation Metrics
277(1)
11.6.2 Experimental Results
277(1)
11.6.3 Statistical Validity
278(1)
11.7 Conclusion
279(1)
Acknowledgment
279(1)
References
280(3)
Index 283
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.

He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.

Himansu Das is working as an as Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He has received his B. Tech and M. Tech degree from Biju Pattnaik University of Technology (BPUT), Odisha, India. He has published several research papers in various international journals and conferences. He has also edited several books of international repute. He is associated with different international bodies as Editorial/Reviewer board member of various journals and conferences. He is a proficient in the field of Computer Science Engineering and served as an organizing chair, publicity chair and act as member of program committees of many national and international conferences. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc., His research interest includes Grid Computing, Cloud Computing, and Machine Learning. He has also 10 years of teaching and research experience in different engineering colleges. Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer Surendra Sai University of Technology (formerly UCE Burla), Odisha, India. He has published more than 100 research articles in various peer reviewed international journals, conferences, and book chapters. He has edited 10 books for publishers including Elsevier, Springer, and IGI Global. At present, he has more than 10 years of teaching experience in the field of computer science and information technology. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and his areas of interest include data science, data mining, machine learning, deep learning, computational intelligence (CI), and CIs applications in science and engineering. He has served as Guest Editor of various special issues of journals such as Information Fusion (Elsevier), Neural Computing and Applications (Springer), Evolutionary Intelligence (Springer), International Journal of Computational Intelligence Studies (Inderscience), and International Journal of Swarm Intelligence (Inderscience). He is an active reviewer of various journals from publishers including IEEE Transactions, Elsevier, Springer, and Inderscience. Currently, he is undertaking a major research project as Principal Investigator, which is funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India. Dr. Himansu Sekhar Behera is currently working as an Associate Professor and Head of the Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), India. He received his Doctor of Philosophy in Engineering (Ph.D.) from Biju Pattnaik University of Technology (BPUT), India. His research and development experience includes over 19 years in academia spanning different technical Institutes in India. His research interests include Data Mining, Soft Computing, Evolutionary Computation, Machine Intelligence and Distributed Systems. He has authored or co-authored over 100 research papers various international conferences and journals, as well as contributing several book chapters. He has edited 11 books and serves as an associate editor / member of the editorial board of various international journals and also guest edited 8 special issues on various topics of Inderscience and IGI Global Journals.
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