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E-grāmata: Blockchain and Machine Learning for e-Healthcare Systems

Edited by (La Trobe University, Australia), Edited by (St. Joseph's College, India), Edited by (Galgotias University, India), Edited by (Galgotias University, India)
  • Formāts: EPUB+DRM
  • Sērija : Healthcare Technologies
  • Izdošanas datums: 21-Dec-2020
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839531156
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Blockchain and Machine Learning for e-Healthcare SystemsPalielināt
  • Formāts: EPUB+DRM
  • Sērija : Healthcare Technologies
  • Izdošanas datums: 21-Dec-2020
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839531156
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Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results.

This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19.



This edited book examines the application of blockchain technology and machine learning algorithms in various healthcare settings. This book covers the basic concepts of Blockchain and Machine Learning, and explores these issues with an eye on improving clinical outcomes and improving the patient's experience.

About the editor xix
Preface xxi
1 Blockchain technology and its relevance in healthcare
1(24)
Poongodi Thangamuthu
Indrakumari Ranganathan
Kiruthika Mani
Suganthi Shanmugam
Suresh Palanimuthu
1.1 Introduction
1(5)
1.1.1 Evolution of blockchain technology
2(1)
1.1.2 Characteristics of blockchain technology
3(1)
1.1.3 Overview of blockchain architecture
4(1)
1.1.4 Merkle tree structure
5(1)
1.2 Basic components of blockchain
6(2)
1.2.1 Cryptographic hash functions
6(1)
1.2.2 Asymmetric-key cryptography
6(1)
1.2.3 Transactions
7(1)
1.2.4 Ledgers
7(1)
1.2.5 Blocks
7(1)
1.3 Consensus models
8(1)
1.3.1 Proof of work
8(1)
1.3.2 Proof of stake
8(1)
1.3.3 Practical byzantine fault tolerance
8(1)
1.3.4 Delegated proof of stake
9(1)
1.3.5 Round robin consensus model
9(1)
1.3.6 Proof of authority (identity) model
9(1)
1.3.7 Proof of elapsed time (PoET) consensus model
9(1)
1.4 Challenges and opportunities of blockchain technology
9(3)
1.4.1 Security and privacy of the data
9(1)
1.4.2 Storage
10(1)
1.4.3 Standardization
10(1)
1.4.4 Scalability
10(1)
1.4.5 Interoperability
10(1)
1.4.6 Key management
10(1)
1.4.7 Blockchain vulnerabilities
11(1)
1.4.8 Social challenges
11(1)
1.4.9 Accountability
11(1)
1.4.10 Accuracy
11(1)
1.4.11 Agility
11(1)
1.4.12 Fighting counterfeit drugs
11(1)
1.4.13 Cost efficient
11(1)
1.4.14 Improving research and development
12(1)
1.5 Types of blockchain
12(5)
1.5.1 Public blockchain
13(1)
1.5.2 Private blockchain
13(1)
1.5.3 Consortium blockchain
14(2)
1.5.4 Permissioned blockchain
16(1)
1.5.5 Permissionless blockchain
17(1)
1.6 Relevance of blockchain for healthcare
17(5)
1.6.1 Blockchain for medical record management
18(1)
1.6.2 Blockchain for medicinal research
19(1)
1.6.3 Blockchain for insurance claims
19(2)
1.6.4 Blockchain for counterfeit drugs
21(1)
1.6.5 Blockchain to prevent future pandemics
22(1)
1.6.6 Blockchain to save cost
22(1)
1.7 Conclusion
22(3)
References
22(3)
2 Privacy issues in blockchain
25(32)
Prabha Selvaraj
Sumathi Doraikannan
Vijay Kumar Burugari
Kanmani Palaniappan
2.1 National and Corporate Support
26(2)
2.2 Asia trade and European trade
28(1)
2.3 Multinational policies vs blockchain
29(6)
2.3.1 Security of blockchain?
30(1)
2.3.2 Shifting security to the end user
31(1)
2.3.3 Trade-offs
31(3)
2.3.4 Key developments of blockchain for voting
34(1)
2.3.5 Improving productivity in agriculture
34(1)
2.3.6 Guarantee straightforwardness, supportability in fishing
34(1)
2.3.7 Cryptocurrency regulations
34(1)
2.3.8 Energy industry
35(1)
2.4 Blockchain approaches to data privacy in healthcare
35(8)
2.4.1 Blockchain for electronic medical record (EMR) data management
37(1)
2.4.2 Blockchain for personal health record (PHR) data management
38(1)
2.4.3 Blockchain for point-of-care genomics
38(1)
2.4.4 Blockchain for EHR data management
39(1)
2.4.5 Fast health-care interoperability resources
40(1)
2.4.6 Health-care blockchain
40(1)
2.4.7 On-chain
40(1)
2.4.8 Off-chain
41(1)
2.4.9 Network is the concern not a database
41(1)
2.4.10 Clear definition of use cases
41(1)
2.4.11 Throughput and scalability
42(1)
2.4.12 Adequate data
42(1)
2.4.13 Blockchain privacy poisoning
42(1)
2.4.14 Consent management and the blockchain
43(1)
2.5 Blockchain privacy poisoning in the context of other privacy issues
43(3)
2.5.1 Who should be accountable for blockchain privacy poisoning?
44(1)
2.5.2 Problems of blockchain security/privacy
44(1)
2.5.3 Challenges
45(1)
2.6 Blockchain security for health data: promises, risks, and future development: blockchain security issues
46(6)
2.6.1 Challenges and limitations
49(3)
2.7 Conclusion
52(5)
References
52(5)
3 Reforming the traditional business network
57(28)
Neethu Narayanan
K.P. Arjun
3.1 Introduction
57(3)
3.2 Applications of blockchain
60(6)
3.2.1 Blockchains in electronic health records (EHR)
60(3)
3.2.2 Blockchains in clinical research
63(1)
3.2.3 Blockchains in medical fraud detection
63(1)
3.2.4 Blockchains in neuroscience
64(1)
3.2.5 Blockchains in pharmaceutical industry and research
65(1)
3.3 Business benefits of blockchain
66(1)
3.4 Reliance on blockchain usage
67(1)
3.4.1 Protection claims
67(1)
3.4.2 Gold supply chain
67(1)
3.4.3 Coordination's activities
68(1)
3.5 Market resistance to blockchain
68(2)
3.5.1 Resistance
68(1)
3.5.2 Level support and resistance
68(1)
3.5.3 Polarity
69(1)
3.6 Role of blockchain in healthcare
70(3)
3.6.1 Drug supply chain
71(1)
3.6.2 Clinical data exchange and interoperability
71(1)
3.6.3 Billing and claims management
71(1)
3.6.4 Cybersecurity and healthcare IoT
72(1)
3.6.5 Population health research and pharma clinical trials
72(1)
3.7 Blockchain in hospital management services
73(1)
3.7.1 Blockchain in healthcare
73(1)
3.7.2 New business opportunities
73(1)
3.7.3 Electronic medical records
73(1)
3.7.4 Guideline compliance
74(1)
3.7.5 Decreased billing and speedy claim settlement
74(1)
3.7.6 Decrease in information risks
74(1)
3.7.7 Coordination of data
74(1)
3.8 Blockchain---the new age business disruptor
74(7)
3.8.1 3D printing
75(1)
3.8.2 Accounting
76(1)
3.8.3 Agriculture
76(1)
3.8.4 Art
77(1)
3.8.5 Credit management
78(1)
3.8.6 Compliance
79(1)
3.8.7 The Internet of Things (IoT)---connected devices
79(2)
3.9 Conclusion
81(4)
References
81(4)
4 A deep dive into Hyperledger
85(24)
Swathi Punathumkandi
Venkatesan Meenakshi Sundaram
Panneer Prabhavathy
4.1 Hyperledger Frameworks
85(10)
4.1.1 Hyperledger Besu
86(1)
4.1.2 Hyperledger Burrow
87(2)
4.1.3 Hyperledger Fabric
89(1)
4.1.4 Hyperledger Indy
90(1)
4.1.5 Hyperledger Iroha
91(1)
4.1.6 Hyperledger Sawtooth
91(2)
4.1.7 Hyperledger Grid
93(2)
4.2 Hyperledger Libraries
95(1)
4.2.1 Hyperledger Aries
95(1)
4.2.2 Hyperledger Quilt
95(1)
4.2.3 Hyperledger Transact
95(1)
4.2.4 Hyperledger Ursa
95(1)
4.3 Hyperledger Tools
96(1)
4.3.1 Hyperledger Avalon
96(1)
4.3.2 Hyperledger Caliper
96(1)
4.3.3 Hyperledger Cello
96(1)
4.3.4 Hyperledger Explorer
97(1)
4.3.5 Hyperledger Composer
97(1)
4.4 Blockchain in enterprise
97(4)
4.4.1 Use cases
98(3)
4.5 Blockchain in e-healthcare
101(3)
4.5.1 Improve medical record access
101(1)
4.5.2 Improve clinical trials
102(1)
4.5.3 Improve drug traceability
103(1)
4.6 An example of healthcare data management using IBM blockchain platform
104(5)
References
106(3)
5 Machine learning
109(28)
Deepa Chinnasamy
Saraswathi Devarajan
5.1 Introduction
109(3)
5.1.1 Machine learning life cycle
110(2)
5.1.2 Components in machine learning
112(1)
5.2 Different types of learning
112(8)
5.2.1 Supervised learning
112(3)
5.2.2 Unsupervised learning
115(2)
5.2.3 Reinforcement learning
117(3)
5.3 Types of machine learning algorithms
120(14)
5.3.1 Classification algorithms
120(2)
5.3.2 Regression algorithm
122(1)
5.3.3 Dimensionality reduction algorithm
123(1)
5.3.4 Clustering algorithms
124(3)
5.3.5 Reinforcement algorithm
127(2)
5.3.6 Machine learning in healthcare
129(2)
5.3.7 Advantages and disadvantages of machine learning
131(2)
5.3.8 Limitations of ML in healthcare industry
133(1)
5.4 Conclusion
134(3)
References
135(2)
6 Machine learning in blockchain
137(24)
Kolla Bhanu Prakash
Vadla Pradeep Kumar
Venkata Raghavendra Naga Pawan
6.1 Introduction
138(3)
6.1.1 What is machine learning?
138(1)
6.1.2 Importance of ML in blockchain
139(1)
6.1.3 Merits and demerits
140(1)
6.2 Types of ML
141(1)
6.2.1 Supervised learning
141(1)
6.2.2 Unsupervised learning
141(1)
6.2.3 Reinforcement learning
141(1)
6.3 Different ML algorithms
142(5)
6.3.1 Linear regression
142(1)
6.3.2 Logistic regression
143(2)
6.3.3 Decision tree and SVM
145(1)
6.3.4 Naive Bayes
146(1)
6.3.5 K-Nearest neighbor
147(1)
6.3.6 K-Means
147(1)
6.3.7 Gradient boosting algorithms---GBM, XGBoost, LightGBM, CatBoost
147(1)
6.4 Significance of ML in the health-care industry
147(8)
6.4.1 Identifying diseases and diagnosis
148(1)
6.4.2 Drug discovery and manufacturing
149(1)
6.4.3 Medical imaging diagnosis
150(1)
6.4.4 Personalized medicine
151(1)
6.4.5 Machine-learning-based behavioral modification
152(1)
6.4.6 Smart health records
152(1)
6.4.7 Clinical trial and research
153(1)
6.4.8 Crowd-sourced data collection, better radiotherapy and outbreak prediction
154(1)
6.5 Implementation difficulties of using ML in healthcare
155(2)
6.6 Applications and future scope of research
157(1)
6.7 Conclusion
158(3)
References
158(3)
7 Framework for approaching blockchain in healthcare using machine learning
161(24)
Vinolyn Vijaykumar
Indrakumari Ranganathan
Lucia Agnes Beena Thomas
7.1 Introduction
161(1)
7.1.1 Introduction to machine learning
162(1)
7.1.2 Introduction to blockchain
162(1)
7.2 The steps in machine learning
162(2)
7.3 Gathering health data
164(3)
7.3.1 Influence of data assemblage in healthcare
164(2)
7.3.2 Recent trends in data collection
166(1)
7.3.3 Healthcare datasets
166(1)
7.4 Data preparation
167(3)
7.4.1 Benefits of data preparation and the cloud
167(1)
7.4.2 Data preparation steps
168(2)
7.5 Choosing a model
170(4)
7.5.1 Types of machine learning algorithms
170(1)
7.5.2 Most familiar machine learning algorithms
171(2)
7.5.3 Need for models in healthcare using blockchain
173(1)
7.6 Training
174(2)
7.6.1 The purpose of train/test sets
174(2)
7.6.2 Blockchain for privacy in healthcare
176(1)
7.6.3 Quantum of training data requirements
176(1)
7.7 Evaluation
176(2)
7.7.1 Evaluation metrics
177(1)
7.7.2 Evaluation metrics and assessment of machine learning algorithms in healthcare
177(1)
7.8 Parameter tuning
178(1)
7.9 Predictive analytics
178(2)
7.9.1 Requirement collection
178(1)
7.9.2 Data collection
179(1)
7.9.3 Data analysis and massaging
179(1)
7.9.4 Statistics, machine learning
179(1)
7.9.5 Predictive modelling
180(1)
7.9.6 Prediction and monitoring
180(1)
7.10 Benefits of integrating machine learning and blockchain
180(1)
7.11 Conclusion
181(4)
References
181(4)
8 Reforming the traditional business network
185(26)
K.P. Arjun
N.M. Sreenarayanan
K. Sampath Kumar
R. Viswanathan
8.1 Introduction
185(2)
8.2 Artificial intelligence in healthcare
187(6)
8.2.1 Artificial intelligence doctors
187(1)
8.2.2 AI---robot treatment
188(2)
8.2.3 AR/VR treatment
190(2)
8.2.4 Non-adherence to prescriptions
192(1)
8.3 Blockchain in healthcare
193(3)
8.3.1 Blockchain in healthcare
193(1)
8.3.2 Medical credential tracking
194(1)
8.3.3 Drug trials
195(1)
8.4 Linear algebra in ML
196(6)
8.4.1 Dataset and data files
197(1)
8.4.2 Images and photographs
198(1)
8.4.3 One-hot encoding
199(1)
8.4.4 Applications
200(2)
8.5 New medical imaging modalities
202(4)
8.5.1 Multivalued data images
202(1)
8.5.2 Phase contrast magnetic resonance angiography (MRA)
203(1)
8.5.3 Diffusion tensor MRI
203(1)
8.5.4 Federated tensor factorization
204(2)
8.6 Medical appliance of norms
206(5)
8.6.1 Significance of medical devices
206(1)
8.6.2 Medical device safety
207(1)
8.6.3 Global Harmonization Task Force
207(1)
8.6.4 Classification of medical devices
208(1)
References
209(2)
9 Healthcare analytics
211(20)
Yogesh Sharma
Balusamy Balamurugan
Sreeji
9.1 Introduction
211(2)
9.2 Analytics
213(4)
9.2.1 Descriptive analytics
214(1)
9.2.2 Predictive analytics
215(1)
9.2.3 Perspective analysis
215(2)
9.3 Emerging technologies in healthcare analytics
217(5)
9.3.1 Big data technology in healthcare analytics
218(1)
9.3.2 Internet of Things in healthcare analytics
219(2)
9.3.3 Artificial intelligence in healthcare
221(1)
9.3.4 Blockchain in healthcare
221(1)
9.4 History of healthcare analytics
222(1)
9.5 Exploring software for healthcare analytics
223(3)
9.5.1 Anaconda
224(1)
9.5.2 SQLite
225(1)
9.6 Challenges with healthcare analytics
226(1)
9.6.1 High-dimensional data
226(1)
9.6.2 Irregularities in data
226(1)
9.6.3 Missing data
227(1)
9.7 Conclusion
227(4)
References
227(4)
10 Blockchain for healthcare
231(36)
Anupam Tiwari
Usha Batra
10.1 Introduction
231(2)
10.1.1 Bitcoin blockchain
232(1)
10.1.2 Block
232(1)
10.1.3 Chain
232(1)
10.2 Ethereum blockchain
233(1)
10.3 Contracts and healthcare: the arising need of smart contracts
234(7)
10.3.1 Smart contracts
234(4)
10.3.2 Zero-knowledge-proofs and smart contracts
238(1)
10.3.3 Ricardian contracts for healthcare
239(1)
10.3.4 Hybrid smart--Ricardian contracts
240(1)
10.4 Applications of blockchain
241(1)
10.5 Healthcare data
241(2)
10.5.1 Structured data sets
242(1)
10.5.2 Non-structured data sets
242(1)
10.6 Popular resources for gathering healthcare data
243(1)
10.7 Need of healthcare data
243(1)
10.8 Services offered by the blockchain in healthcare
244(2)
10.8.1 Data sharing and privacy issues
244(1)
10.8.2 Longitudinal patient records and health data accuracy
245(1)
10.8.3 Drug track ability
245(1)
10.8.4 Fake medical credentials
245(1)
10.8.5 Claims processing
245(1)
10.8.6 Supply chain management
246(1)
10.8.7 Interoperability of data among medical institutes
246(1)
10.9 Medicines and supply chain tracking enabled by blockchain
246(1)
10.10 Data security concerns in EMR and healthcare domain
247(1)
10.11 Choices of blockchain platforms for healthcare
248(6)
10.11.1 Ethereum
249(1)
10.11.2 IBM® blockchain
250(1)
10.11.3 Hyperledger
250(1)
10.11.4 Hydrachain
250(1)
10.11.5 R3 Corda
251(1)
10.11.6 Multichain
252(1)
10.11.7 BigchainDB
252(1)
10.11.8 OpenChain
253(1)
10.11.9 Quorum blockchain platform
253(1)
10.11.10 EOS blockchain platform
253(1)
10.11.11 Internet-of-Things application (IOTA) blockchain platform
254(1)
10.12 Major healthcare blockchain use cases under development
254(2)
10.13 Storage challenges and need for inter planetary file system (IPFS) enabled blockchain for healthcare
256(1)
10.14 IPFS
256(1)
10.15 Why do we need IPFS?
257(1)
10.16 Blockchain and IPFS
257(1)
10.17 Challenges and roadblocks to the realisation of blockchain-enabled healthcare
258(3)
10.18 Conclusion
261(6)
References
261(6)
11 Improved interop blockchain applications for e-healthcare systems
267(28)
Jesu Rethnam Rethna Virgil Jeny
Kaushik Sekaran
Sai Srujan Dandyala
11.1 Introduction
268(2)
11.1.1 How blockchain is used in healthcare?
269(1)
11.1.2 Challenges in interoperability between various sections of healthcare system
269(1)
11.2 Literature review
270(5)
11.2.1 Electronic health records
270(1)
11.2.2 Drug tracking
271(1)
11.2.3 Blockchain in future healthcare
271(1)
11.2.4 Blockchain and cryptocurrencies
271(4)
11.3 Proposed method
275(15)
11.3.2 Smart-contract system design
275(2)
11.3.3 Smart contract implementation
277(4)
11.3.4 Drug traceability using blockchain
281(6)
11.3.5 Clinical trials using blockchain
287(3)
11.4 Conclusion and future scope
290(5)
References
291(4)
12 Blockchain: lifeline care for breast cancer patients in developing countries
295(24)
Hamsagayathri Palanisamy
Sampath Palaniswami
Godlin Atlas
Perarasi Sambantham
Gayathri Rajendran
Sowmiya Senthilvel
12.1 Introduction
296(1)
12.2 Blockchain
297(3)
12.2.1 Key attributes
297(1)
12.2.2 Kind of blockchains
298(1)
12.2.3 Agreement instruments
298(1)
12.2.4 Shrewd agreements
299(1)
12.2.5 The capability of blockchain in the human services space
299(1)
12.3 Healthcare data management
300(8)
12.3.1 Blockchain-based smart contracts for healthcare
301(2)
12.3.2 The process for issuing and filling of medical prescriptions
303(1)
12.3.3 Sharing laboratory test/results data
303(2)
12.3.4 Enabling effective communication between patients and service providers
305(1)
12.3.5 Smart-contracts-based clinical trials
306(2)
12.4 Healthcare data management
308(2)
12.4.1 Medication revelation and pharmaceutical research
308(1)
12.4.2 Flexibly chain and counterfeit medications discovery
309(1)
12.5 Challenges and future scope
310(3)
12.5.1 Interoperability and integration with the legacy systems
310(1)
12.5.2 Selection and motivating forces for support
311(1)
12.5.3 Uncertain expense of activity
311(1)
12.5.4 Regulation
312(1)
12.5.5 Governance
312(1)
12.5.6 Scaling
312(1)
12.6 Conclusion
313(6)
References
314(5)
13 Machine learning for health care
319(24)
B.K.S.P. Kumar Raju Alluri
13.1 Machine learning pipelining
319(1)
13.2 Applications of ML in health care
320(1)
13.3 Common machine learning approaches in machine learning
321(2)
13.3.1 Artificial neural networks
321(1)
13.3.2 Tree-like reasoning
321(1)
13.3.3 Other common ML algorithms
322(1)
13.4 Application of machine learning in health care
323(8)
13.4.1 COVID-19---interpretation, detection and drug discovery using machine learning
323(8)
13.5 Breaking the blackbox of neural networks through explainable AI
331(6)
13.6 Conclusion
337(6)
References
338(5)
14 Machine learning in healthcare diagnosis
343(24)
Sugumaran Muthukumarasamy
Ananth Kumar Tamilarasan
John Ayeelyan
M. Adimoolam
14.1 Introduction
344(1)
14.1.1 State of art of diagnosing system using machine learning
344(1)
14.2 Heart diseases diagnosing system using machine learning
345(8)
14.2.1 Various methods for diagnosis of heart disease using ML
349(4)
14.3 Breast cancer diagnosing system using machine learning
353(3)
14.3.1 k-NN method for breast cancer prediction
355(1)
14.4 Neurological diseases diagnosing system using machine learning
356(6)
14.4.1 Detecting the neurodegenerative diseases and the traumatic brain related injuries using D-CNN
357(3)
14.4.2 Diagnosis using 3D-CNN
360(1)
14.4.3 Training of 3D sparse autoencoder and 3D-CNN
361(1)
14.5 Challenges and future direction of medical diagnosing system
362(1)
14.6 Conclusion
362(5)
References
363(4)
15 Python for healthcare analytics made simple
367(26)
Sumathi Doraikannan
Prabha Selvaraj
15.1 Introduction
368(2)
15.1.1 Data
368(1)
15.1.2 Importance of data quality in healthcare
368(1)
15.1.3 Elements of data quality
369(1)
15.1.4 Ensuring data and information quality
369(1)
15.2 Data extraction
370(8)
15.2.1 Implementing NER with NLTK
371(4)
15.2.2 Implementing Named Entity Recognition using SpaCy
375(3)
15.3 Data visualization and tools
378(4)
15.4 Advanced visualization methods
382(4)
15.5 Data analytics
386(2)
15.5.1 Descriptive analytics
386(1)
15.5.2 Diagnostic analytics
386(1)
15.5.3 Predictive analytics
386(1)
15.5.4 Prescriptive analytics
387(1)
15.6 Healthcare and technology: open issues
388(2)
15.6.1 Data remanence
389(1)
15.6.2 Data interoperability
389(1)
15.6.3 Data staging
389(1)
15.6.4 Application of big data in biomedical research
389(1)
15.7 Conclusion
390(3)
References
390(3)
16 Identification and classification of hepatitis C virus: an advance machine-learning-based approach
393(24)
Janmenjoy Nayak
Pemmada Suresh Kumar
Dukka Karun Kumar Reddy
Bighnaraj Naik
16.1 Introduction
394(2)
16.2 Literature survey
396(2)
16.2.1 Artificial neural network
396(1)
16.2.2 Random forest
397(1)
16.2.3 Decision tree
397(1)
16.2.4 Support vector machine
397(1)
16.3 Proposed methodology
398(3)
16.3.1 Bagging classifier
398(3)
16.4 Experimental setup
401(5)
16.4.1 Data preprocessing
401(1)
16.4.2 Dataset description
402(1)
16.4.3 Attribute information
403(1)
16.4.4 Evaluation metrics
404(2)
16.4.5 Environmental setup
406(1)
16.5 Result analysis
406(6)
16.6 Conclusion
412(5)
References
412(5)
17 Data visualization using machine learning for efficient tracking of pandemic -- COVID-19
417(26)
Supriya Khaitan
Priyanka Shukla
Anamika Mitra
T. Poongodi
Rashi Agarwal
17.1 Introduction
417(1)
17.2 Data preprocessing
418(3)
17.2.1 Importance of data preprocessing
419(1)
17.2.2 Data preprocessing consist of following steps
419(2)
17.3 Exploratory data analysis
421(1)
17.3.1 Univariate analysis
421(1)
17.3.2 Bivariate analysis
422(1)
17.4 Data visualization techniques
422(3)
17.4.1 Box plot
423(1)
17.4.2 Charts
423(2)
17.5 Maps
425(1)
17.5.1 Thickness maps
426(1)
17.5.2 Tree map
426(1)
17.6 Disperse plot
426(1)
17.7 Gantt chart
426(1)
17.8 Importance of data visualization in healthcare
427(1)
17.8.1 Uses of data visualization
428(1)
17.9 COVID-19 gripping the world
428(4)
17.10 COVID-19 India situation
432(6)
17.11 Issue and challenges
438(1)
17.12 Conclusion
439(4)
References
439(4)
Index 443
Dr. Balamurugan Balusamy has served up to the position of Associate Professor in his stint of 14 years of experience with VIT University, Vellore, Tamil Nadu, India. He has completed his Bachelors, Masters and Ph.D. Degrees from top premier institutions. His passion is teaching and adapts different design thinking principles while delivering his lectures. He has done around 30 books on various technologies and visited more than 15 countries for his technical discourse. He has several top notch conferences in his resume and has published over 150 quality journal, conference and book chapters combined. He serves in the advisory committee for several startup and forums and does consultancy work for industry on Industrial IoT. He has given over 175 talks in various events and symposium. He is currently working as a professor in Galgotias University and teaches students, does research on blockchain and IoT.



Dr. Naveen Chilamkurti is currently Acting Head of Department, Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, Australia. He obtained his Ph.D. degree from La Trobe University. He is also the Inaugural Editor-in-Chief for International Journal of Wireless Networks and Broadband Technologies launched in July 2011. He has published about 165 journal and conference papers. His current research areas include intelligent transport systems (ITS), wireless multimedia, wireless sensor networks, and so on. He currently serves on the editorial boards of several international journals. He is a Senior Member of IEEE. He is also an Associate editor for Wiley IJCS, SCN, Inderscience JETWI, and IJIPT.



Dr. T. Lucia Agnes Beena is working as an Assistant Professor in the department of Information Technology, St. Joseph's College, Tiruchirappalli, Tamil Nadu, India. She has 18 years of teaching experience and 6 years of research experience. She has published number of research articles in Scopus Indexed Journals. She authored one book and published few chapters with reputed publishers. Her areas of interest are cloud computing, big data and psychology of computer programming.



Dr. T. Poongodi is working as an Associate Professor in the School of Computing Science and Engineering, Galgotias University, Delhi - NCR, India. She has completed Ph.D. in Information Technology (Information and Communication Engineering) from Anna University, Tamil Nadu, India. She is a pioneer researcher in the areas of big data, wireless ad-hoc network, internet of things, network security and blockchain technology. She has published more than 50 papers in various international journals, national/international conferences, and book chapters in CRC Press, IGI Global, Springer, Elsevier, Wiley, De Gruyter and edited books in CRC, IET, Wiley, Springer.
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