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Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics: Concepts, Methodologies, Tools and Applications [Kõva köide]

Edited by (GITAM University, Visakhapatnam, India), Edited by (Orissa Engineering College (OEC) Bhubaneswar, India), Edited by (Sri Sri University, India), Edited by (Addis Ababa Science & Technology University, Addis Ababa, Ethiopia)
Teised raamatud teemal:
Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics: Concepts, Methodologies, Tools and ApplicationsSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781119791737.html
Märksõnad:
BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics.

The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data.

The 12 chapters in Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT).

New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches.

Audience

Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
Preface xiii
1 An Introduction to Big Data Analytics Techniques in Healthcare
1(20)
Anil Audumbar Pise
1.1 Introduction
1(2)
1.2 Big Data in Healthcare
3(2)
1.3 Areas of Big Data Analytics in Medicine
5(4)
1.3.1 Genomics
6(1)
1.3.2 Signal Processing
7(1)
1.3.3 Image Processing
8(1)
1.4 Healthcare as a Big Data Repository
9(1)
1.5 Applications of Healthcare Big Data
10(6)
1.5.1 Electronic Health Records (EHRs)
10(1)
1.5.2 Telemedicine
11(1)
1.5.3 NoSQL Database
12(1)
1.5.4 Framework for Reconstructing Epidemiological Dynamics (FRED)
12(1)
1.5.5 Advanced Risk and Disease Management
13(1)
1.5.6 Digital Epidemiology
13(1)
1.5.7 Internet of Things (IoT)
13(1)
1.5.7.1 IoT for Health Insurance Companies
14(1)
1.5.7.2 IoT for Physicians
14(1)
1.5.7.3 IoT for Hospitals
15(1)
1.5.7.4 IoT for Patients
15(1)
1.5.8 Improved Supply Chain Management
16(1)
1.5.9 Developing New Therapies and Innovations
16(1)
1.6 Challenges in Big Data Analytics
16(1)
1.7 Big Data Privacy and Security
17(1)
1.8 Conclusion
18(1)
1.9 Future Work
18(3)
References
18(3)
2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia
21(26)
Sudhir Kumar Mohapatra
Srinivas Prasad
Getachew Mekuria Habtemariatn
Mohammed Siddique
2.1 Introduction
22(1)
2.2 Literature Review
23(2)
2.3 Methodology and Data Source
25(3)
2.3.1 Study Area
26(1)
2.3.2 Source of Data
26(1)
2.3.3 Variables Included in the Study
26(1)
2.3.4 Building a Predictive Model
26(2)
2.4 Implementation and Results
28(16)
2.4.1 Missing Value Handling
30(1)
2.4.2 Feature Selection Methods
30(1)
2.4.3 Features Importance Rank
31(1)
2.4.4 Data Split
31(2)
2.4.5 Imbalanced Data Handling
33(2)
2.4.6 Make Predictions on Unseen Test Data
35(1)
2.4.6.1 Naive Bayes Classifier: Prediction on Test Data
35(2)
2.4.6.2 C5.0 Classifier on Train Dataset
37(1)
2.4.6.3 Rules From Decision Trees
38(1)
2.4.6.4 SVM Classifier: Unbalanced and Balanced Train Dataset
39(2)
2.4.6.5 Random Forest Model: On Train Dataset
41(1)
2.4.7 Evaluation
42(2)
2.5 Conclusion
44(3)
References
44(3)
3 Pre-Trained CNN Models in Early Alzheimer's Prediction Using Post-Processed MRI
47(50)
Kalyani Gunda
Pradeepini Gera
3.1 Introduction
48(3)
3.1.1 Background
48(3)
3.2 Experimental Study
51(4)
3.2.1 OASIS Longitudinal Data
51(1)
3.2.1.1 Feature Characteristics
52(2)
3.2.2 Alzheimer's 4-Class-MRI-Dataset
54(1)
3.3 Data Exploration
55(6)
3.3.1 Features Description
55(6)
3.4 OASIS Dataset Pre-Processing
61(8)
3.4.1 Features Selection
62(1)
3.4.2 Feature Transform
62(1)
3.4.2.1 MinMaxScaler
63(1)
3.4.3 Model Selection
64(1)
3.4.3.1 Decision Tree Classification
64(1)
3.4.3.2 Ensemble Machine Learning
65(1)
3.4.3.3 Random Forest Classifier
65(1)
3.4.4 Model Fitting
66(1)
3.4.5 Evaluation Metric/Model Evaluation
67(2)
3.5 Alzheimer's 4-Class-MRI Features Extraction
69(1)
3.6 Alzheimer 4-Class MRI Image Dataset
69(11)
3.6.1 Image Processing
69(2)
3.6.2 Classification of 4-CLASS-MRI
71(3)
3.6.2.1 AlexNet
74(1)
3.6.2.2 VGG-16
75(1)
3.6.2.3 Inception (GoogLeNet)
76(1)
3.6.2.4 Residual Network ("RESNET")
77(1)
3.6.2.5 MobileNetV2
78(1)
3.6.2.6 NASANet (Neural Architecture Search Network)
79(1)
3.7 RMSProp (Root Mean Square Propagation)
80(1)
3.8 Activation Function
81(1)
3.9 Batch Normalization
81(1)
3.10 Dropout
81(1)
3.11 Result---I
82(7)
3.11.1 Result---II
84(5)
3.12 Conclusion and Future Work
89(8)
Acknowledgement
89(1)
References
90(7)
4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging
97(22)
Birendra Biswal
Raveendra T.
Dwiti Krishna Bebarta
Geetha Pavani P.
P.K. Biswal
4.1 Introduction
98(2)
4.2 Basics of Proposed Methods
100(7)
4.3 Experimental Results and Discussion
107(8)
4.4 Conclusion
115(4)
References
116(3)
5 Analysis of Healthcare Systems Using Computational Approaches
119(28)
Hemanta Kumar Bhuyan
Subhendu Kumar Pani
5.1 Introduction
120(4)
5.1.1 Diagnosis Process in Healthcare Systems
120(1)
5.1.2 Issues of Healthcare
120(2)
5.1.3 Clinical Diagnosis Based on Soft Computing
122(1)
5.1.3.1 Neural Network and Fuzzy Healthcare Systems
122(1)
5.1.3.2 Systems of Fuzzy-Genetic Algorithms (F-GA)
123(1)
5.1.3.3 Genetic Algorithm Systems and Neural Networks (NNGA)
123(1)
5.1.3.4 Genetic Algorithm, Fuzzy Logic and Neural Network (NN-FL-GA)
123(1)
5.1.3.5 Tool for Big Data Analytics
124(1)
5.2 AI & ML Analysis in Health Systems
124(3)
5.3 Healthcare Intellectual Approaches
127(6)
5.3.1 AI and ML Roles in the Healthcare System
127(2)
5.3.2 Medical ML Medicine
129(1)
5.3.3 Clinical System Growth
130(1)
5.3.4 Clinical Data Development Using AI
130(1)
5.3.5 EHR Disease Detection
130(1)
5.3.6 Cognitive Cancer Approaches
130(1)
5.3.7 Effective EHR Operations
131(1)
5.3.8 Deep Learning Approach (DL) in the Clinical System
131(1)
5.3.9 Healthcare Data Transformation
131(2)
5.3.10 Prediction of Cancer
133(1)
5.4 Precision Approaches to Medicine
133(1)
5.4.1 EMR Analysis Medicine
133(1)
5.4.2 AI-Based Medicine Accuracy
134(1)
5.4.3 Tumor Cell Visual Evaluation
134(1)
5.5 Methodology of AI, ML With Healthcare Examples
134(2)
5.6 Big Analytic Data Tools
136(5)
5.6.1 Hadoop-Based Health Industry Tools
138(1)
5.6.2 Healthcare System Architecture
138(3)
5.7 Discussion
141(1)
5.8 Conclusion
142(5)
References
143(4)
6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy
147(40)
Shrikaant Kulkarni
6.1 Introduction
148(1)
6.2 AI Methods
149(7)
6.2.1 Machine Learning & Artificial Neural Networks (ML & ANN)
149(2)
6.2.2 Natural Language Processing (NLP)
151(1)
6.2.3 Machine Perception & Sensing
152(1)
6.2.4 Affective Computing
152(1)
6.2.5 Virtual & Augmented Reality (VR & AR)
153(1)
6.2.6 Cloud Computing & Wireless Technology
154(1)
6.2.7 Robotics
154(1)
6.2.8 Brain-Computer Interfaces (BCIs)
154(1)
6.2.9 Supercomputing & Simulation of Brain
155(1)
6.3 Turing Test
156(1)
6.4 Barriers to Technologies
157(1)
6.5 Advantages of AI for Behavioral & Mental Healthcare
157(1)
6.6 Enhanced Self-Care & Access to Care
158(2)
6.6.1 Care Customization
158(1)
6.6.2 Economic Benefits
159(1)
6.7 Other Considerations
160(1)
6.8 Expert Systems in Mental & Behavioral Healthcare
161(4)
6.8.1 Historical Perspectives
162(3)
6.9 Dynamical Approaches to Clinical AI and Expert Systems
165(8)
6.9.1 Temporal Modeling
165(1)
6.9.2 Practical Global Clinical Applications
165(2)
6.9.3 Multi-Agent Model Dedicated to Personalized Medicine
167(1)
6.9.4 Technology-Enabled Clinicians
168(1)
6.9.5 Overview of Dynamical Approaches
168(1)
6.9.6 Cognitive Computing in Healthcare
169(2)
6.9.7 Emerging Technologies & Clinical AI
171(1)
6.9.8 Ethics and Futuristic Challenges
172(1)
6.10 Conclusion
173(2)
6.11 Future Prospects
175(12)
References
176(11)
7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19)
187(18)
Shanmuk Srinivas Amiripalli
Vishnu Vardhan Reddy Kollu
Ritika Prasad
Mukkamala S.N. V. Jitendra
7.1 Introduction
188(1)
7.1.1 Corona Viruses
188(1)
7.1.2 Epidemiological Modeling Using Graph Theory
189(1)
7.2 Related Work
189(1)
7.3 Proposed Frameworks
190(4)
7.3.1 Infection Spreading Model
190(1)
7.3.2 Relation between Recovery Time and Interaction of Antivirus Nodes
191(1)
7.3.3 Proposed Algorithm
192(1)
7.3.4 Detail Explanation of Algorithm
193(1)
7.4 Results and Discussion
194(7)
7.5 Conclusion
201(4)
References
201(4)
8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information
205(32)
Sowjanya Naidu K.
Srinivasa L. Chakravarthy
8.1 Introduction
206(6)
8.1.1 Basics of Blockchain Technology
206(3)
8.1.2 Distributed Consensus Protocol
209(2)
8.1.3 Smart Contracts
211(1)
8.1.3.1 How Do Smart Contracts Work?
211(1)
8.1.4 Ethereum and Smart Contracts
212(1)
8.2 Related Work
212(4)
8.3 Need for Blockchain in Healthcare
216(3)
8.4 Proposed Frameworks
219(4)
8.5 Use Cases
223(6)
8.6 Discussions
229(2)
8.7 Challenges and Limitations
231(1)
8.8 Future Work
231(1)
8.9 Conclusion
232(5)
References
233(4)
9 An Epidemic Graph's Modeling Application to the COVID-19 Outbreak
237(20)
Hemanta Kumar Bhuyan
Subhendu Kumar Pani
9.1 Introduction
237(2)
9.2 Related Work
239(1)
9.3 Theoretical Approaches
240(3)
9.3.1 Graph Convolutional Networks
241(1)
9.3.2 Recurrent Neural Networks
241(1)
9.3.3 Epidemic Modeling
242(1)
9.4 Frameworks
243(3)
9.4.1 Use the Data Model
243(1)
9.4.2 Problem Formulation
244(1)
9.4.3 Proposed Architecture
244(2)
9.5 Evaluation of COVID-19 Outbreak
246(4)
9.5.1 Used Datasets
246(1)
9.5.2 Evolving an Epidemic
246(4)
9.5.3 Predicted Analysis of the Infected Individuals
250(1)
9.6 Conclusions and Future Works
250(7)
References
252(5)
10 Big Data and Data Mining in e-Health: Legal Issues and Challenges
257(18)
Amita Verma
Arpit Bansal
Object of Study
257(1)
10.1 Introduction
258(2)
10.2 Big Data and Data Mining in e-Health
260(2)
10.3 Big Data and e-Health in India
262(1)
10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health
263(8)
10.4.1 Right to Privacy
264(1)
10.4.2 Data Privacy Laws
265(5)
10.4.3 Liability of the Intermediary
270(1)
10.5 Big Data and Issues of Privacy in e-Health
271(1)
10.6 Conclusion and Suggestions
272(3)
References
273(2)
11 Basic Scientific and Clinical Applications
275(30)
Manna Sheela Rani Chetty
Kiran Babu
11.1 Introduction
275(8)
11.2 Case Study-1: Continual Learning Using ML for Clinical Applications
283(3)
11.3 CaseStudy-2
286(3)
11.4 Case Study-3: ML Will Improve the Radiology Patient Experience
289(3)
11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization
292(3)
11.6 Case Study-5: ML will Benefit All Medical Imaging `ologies'
295(3)
11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data
298(2)
11.8 Conclusion
300(5)
References
301(4)
12 Healthcare Branding Through Service Quality
305(16)
Saraju Prasad
Sunil Dhal
12.1 Introduction to Healthcare
305(2)
12.2 Quality in Healthcare
307(4)
12.2.1 Developing Countries Healthcare Service Quality
308(1)
12.2.2 Affordability of Quality in Healthcare
308(1)
12.2.3 Dimensions of Healthcare Service
309(1)
12.2.4 Healthcare Brand Image
309(1)
12.2.5 Patients' Satisfaction
310(1)
12.2.6 Patients' Loyalty
310(1)
12.3 Service Quality
311(4)
12.3.1 Patient Loyalty with Service Quality in Healthcare
312(1)
12.3.2 Healthcare Policy
313(2)
12.4 Conclusion and Road Ahead
315(6)
References
316(5)
Index 321
Sunil Kumar Dhal, PhD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.

Subhendu Kumar Pani, PhD, is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC) Bhubaneswar, India. He has more than 15 years of teaching and research experience and has published more than 50 international journal articles as well as five authored books, 12 edited books, and eight patents.

Srinivas Prasad, PhD, is a professor in the Department of Computer Science and Engineering at GITAM University, Visakhapatnam, India. He has more than 20 years of teaching experience and published more than 60 publications which include journal articles, conference papers, edited volumes, and book chapters.

Sudhir Kumar Mohapatra, PhD, is an associate professor at Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. Besides 10 years of teaching and research, he spent five years in software development in the banking and education domains.