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Machine Learning, Big Data, and IoT for Medical Informatics [Mīkstie vāki]

Edited by (Department of Computer Science and Engineering, Jaypee University of Information Technology (JUIT), Solan, Himachal Pradesh, India), Series edited by , Edited by (School of Technology Management and Engineering, NMIMS University, Chandigarh Campus, Mumbai, India-;), Edited by
  • Formāts: Paperback / softback, 458 pages, height x width: 235x191 mm, weight: 930 g, Approx. 110 illustrations; Illustrations
  • Sērija : Intelligent Data-Centric Systems
  • Izdošanas datums: 16-Jun-2021
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128217774
  • ISBN-13: 9780128217771
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Machine Learning, Big Data, and IoT for Medical InformaticsPalielināt
  • Formāts: Paperback / softback, 458 pages, height x width: 235x191 mm, weight: 930 g, Approx. 110 illustrations; Illustrations
  • Sērija : Intelligent Data-Centric Systems
  • Izdošanas datums: 16-Jun-2021
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0128217774
  • ISBN-13: 9780128217771
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780128217771.html
Keywords:

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.

In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.

This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.

  • Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems.
  • Includes several privacy preservation techniques for medical data.
  • Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis.
  • Offers case studies and applications relating to machine learning, big data, and health care analysis.
Contributors xvii
Preface xxi
Chapter 1 Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
1(36)
Deepti Lamba
William H. Hsu
Majed Alsadhan
1 Introduction: Predictive analytics for medical informatics
2(8)
1.1 Overview: Goals of machine learning
2(1)
1.2 Current state of practice
3(1)
1.3 Key task definitions
3(4)
1.4 Open research problems
7(3)
2 Background
10(8)
2.1 Diagnosis
10(3)
2.2 Predictive analytics
13(1)
2.3 Therapy recommendation
14(1)
2.4 Automation of treatment
15(1)
2.5 Integrating medical informatics and health informatics
16(2)
3 Techniques for machine learning
18(2)
3.1 Supervised, unsupervised, and semisupervised learning
18(1)
3.2 Reinforcement learning
19(1)
3.3 Self-supervised, transfer, and active learning
20(1)
4 Applications
20(1)
4.1 Test beds for diagnosis and prognosis
20(1)
4.2 Test beds for therapy recommendation and automation
21(1)
5 Experimental results
21(1)
5.1 Test bed
21(1)
5.2 Results and discussion
22(1)
6 Conclusion: Machine learning for computational medicine
22(15)
6.1 Frontiers: Preclinical, translational, and clinical
22(1)
6.2 Toward the future: Learning and medical automation
23(1)
References
23(14)
Chapter 2 Geolocation-aware loT and cloud-fog-based solutions for healthcare
37(16)
Jaydeep Das
1 Introduction
37(2)
2 Related work
39(2)
2.1 Health monitoring system with cloud computing
39(1)
2.2 Health monitoring system with fog computing
39(1)
2.3 Health monitoring system with cloud-fog computing
40(1)
3 Proposed framework
41(6)
3.1 Health data analysis
42(1)
3.2 Geospatial analysis for medical facility
42(3)
3.3 Delay and power consumption calculation
45(2)
4 Performance evaluation
47(3)
5 Conclusion and future work
50(3)
References
51(2)
Chapter 3 Machine learning vulnerability in medical imaging
53(18)
Theodore V. Maliamanis
George A. Papakostas
1 Introduction
53(1)
2 Computer vision
54(2)
3 Adversarial computer vision
56(2)
4 Methods to produce adversarial examples
58(2)
5 Adversarial attacks
60(2)
6 Adversarial defensive methods
62(2)
7 Adversarial computer vision in medical imaging
64(2)
8 Adversarial examples: How to generate?
66(1)
9 Conclusion
66(5)
Acknowledgment
67(1)
References
67(4)
Chapter 4 Skull stripping and tumor detection using 3D U-Net
71(14)
Rahul Gupta
Isha Sharma
Vijay Kumar
1 Introduction
71(3)
1.1 Previous work
72(2)
2 Overview of U-net architecture
74(3)
2.1 3D U-net
74(3)
3 Materials and methods
77(1)
3.1 Dataset
77(1)
3.2 Implementation
77(1)
4 Results
78(4)
4.1 Experimental result
78(1)
4.2 Quantitative result
79(1)
4.3 Qualitative result
79(3)
5 Conclusion
82(3)
References
82(3)
Chapter 5 Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach
85(12)
Apurba Das
S.S. Shylaja
1 Introduction
85(1)
2 Range-domain filtering
86(1)
3 Cross color dominant deep autoencoder (C2DZA) leveraging color spareness and saliency
87(4)
3.1 Evolution of DCM through C2D2A
88(3)
3.2 Inclusion of DCM into principal flow of bilateral filtering
91(1)
4 Experimental results
91(2)
5 Conclusion
93(4)
Acknowledgments
94(1)
References
94(3)
Chapter 6 Estimating the respiratory rate from ECG and PPG using machine learning techniques
97(14)
Wenhan Tan
Anup Das
1 Introduction
97(3)
1.1 Motivation
97(1)
1.2 Background
98(2)
2 Related work
100(3)
3 Methods
103(1)
3.1 Data
103(1)
3.2 Steps
103(1)
3.3 RR signal extraction
104(1)
3.4 Machine learning
104(1)
4 Experimental results
104(1)
5 Discussion and conclusion
105(6)
Acknowledgments
109(1)
References
109(2)
Chapter 7 Machine learning-enabled Internet of Things for medical informatics
111(16)
Ali Nauman
Yazdan Ahmad Qadri
Rashid Ali
Sung Won Kim
1 Introduction
111(3)
1.1 Healthcare Internet of Things
112(2)
2 Applications and challenges of H-IoT
114(5)
2.1 Applications of H-IoT
114(3)
2.2 Challenges of H-IoT system
117(2)
3 Machine learning
119(3)
3.1 Machine learning advancements at the application level of H-IoT
121(1)
3.2 Machine learning advancements at network level of H-IoT
121(1)
4 Future research directions
122(2)
4.1 Novel applications of ML in H-IoT
122(1)
4.2 Research opportunities in network management
123(1)
5 Conclusion
124(3)
References
125(2)
Chapter 8 Edge detection-based segmentation for detecting skin lesions
127(16)
Marwa A. Gaheen
Enas Ibrahim
Ahmed A. Ewees
1 Introduction
127(2)
2 Previous works
129(1)
3 Materials and methods
130(1)
3.1 Elitist-Jaya algorithm
130(1)
3.2 Otsu's method
131(1)
4 Proposed method
131(2)
4.1 Image preprocessing
131(2)
4.2 Edge detection
133(1)
5 Experiment and results
133(7)
5.1 Dataset
133(1)
5.2 Evaluation metrics
133(2)
5.3 Results and discussion
135(3)
5.4 Statistical analysis
138(2)
6 Conclusion
140(3)
References
140(3)
Chapter 9 A review of deep learning approaches in glove-based gesture classification
143(22)
Emmanuel Ayodele
Syed Ali Raza Zaidi
Zhiqiang Zhang
Jane Scott
Des McLernon
1 Introduction
143(2)
2 Data gloves
145(2)
2.1 Early and commercial data gloves
145(1)
2.2 Sensing mechanism in data gloves
146(1)
3 Gesture taxonomies
147(1)
4 Gesture classification
148(12)
4.1 Classical machine learning algorithms
149(3)
4.2 Glove-based gesture classification with classical machine learning algorithms
152(3)
4.3 Deep learning
155(3)
4.4 Glove-based gesture classification using deep learning
158(2)
5 Discussion and future trends
160(1)
6 Conclusion
161(4)
References
162(3)
Chapter 10 An ensemble approach for evaluating the cognitive performance of human population at high altitude
165(14)
Dipankar Sengupta
Vijay Kumar Sharma
Sunil Kumar Hota
Ravi B. Srivastava
Pradeep Kumar Naik
1 Introduction
165(3)
2 Methodology
168(3)
2.1 Data collection
168(2)
2.2 Data processing and feature selection
170(1)
2.3 Differential expression analyses
170(1)
2.4 Association rule mining
170(1)
2.5 Experimental set-up
171(1)
3 Results and discussion
171(3)
3.1 Differential analyses--Cognitive and clinical features
171(2)
3.2 Discovered associative rules
173(1)
3.3 Discussion
173(1)
4 Future opportunities
174(1)
5 Conclusions
175(4)
Acknowledgment
175(1)
References
175(4)
Chapter 11 Machine learning in expert systems for disease diagnostics in human healthcare
179(22)
Arvind Kumar Yadav
Rohit Shukla
Tiratha Raj Singh
1 Introduction
179(4)
2 Types of expert systems
183(1)
3 Components of an expert system
183(2)
4 Techniques used in expert systems of medical diagnosis
185(3)
5 Existing expert systems
188(1)
6 Case studies
188(6)
6.1 Cancer diagnosis using rule-based expert system
188(2)
6.2 Alzheimer's diagnosis using fuzzy-based expert systems
190(4)
7 Significance and novelty of expert systems
194(1)
8 Limitations of expert systems
195(1)
9 Conclusion
195(6)
Acknowledgment
196(1)
References
196(5)
Chapter 12 An entropy-based hybrid feature selection approach for medical datasets
201(14)
Rakesh Raja
Bikash Kanti Sarkar
1 Introduction
201(1)
1.1 Deficiencies of the existing models
202(1)
1.2
Chapter organization
202(1)
2 Background of the present research
202(2)
2.1 Feature selection (FS)
202(2)
3 Methodology
204(2)
3.1 The entropy based feature selection approach
204(2)
4 Experiment and experimental results
206(1)
4.1 Experiment using suggested feature selection approach
207(1)
5 Discussion
207(3)
5.1 Performance analysis of the suggested feature selection approach
207(3)
6 Conclusions and future works
210(5)
Appendix A
210(1)
A.1 Explanation on entropy-based featureextraction approach
211(1)
References
212(3)
Chapter 13 Machine learning for optimizing healthcare resources
215(26)
Abdalrahman Tawhid
Tanya Teotia
Haytham Elmiligi
1 Introduction
215(2)
2 The state of the art
217(3)
2.1 Resource management
217(1)
2.2 Impact on people's health
218(1)
2.3 Exit strategies
219(1)
3 Machine learning for health data analysis
220(1)
4 Feature selection techniques
221(6)
4.1 Filter approach
222(2)
4.2 Wrapper approach
224(3)
4.3 Embedded approach
227(1)
5 Machine learning classifiers
227(1)
5.1 One-class vs. multiclass classification
227(1)
5.2 Supervised vs. unsupervised learning
228(1)
6 Case studies
228(4)
6.1 Experimental setup
228(1)
6.2 Case study I: Diabetes data analysis
228(4)
7 Case study 2: COVID-19 data analysis
232(3)
8 Summary and future directions
235(6)
References
237(4)
Chapter 14 Interpretable semisupervised classifier for predicting cancer stages
241(20)
Isel Grau
Dipankar Sengupta
Ann Nowe
1 Introduction
241(3)
2 Self-labeling gray box
244(2)
3 Data preparation
246(3)
4 Experiments and discussion
249(6)
4.1 Influence of clinical and proteomic data on the prediction of cancer stage
251(1)
4.2 Influence of unlabeled data on the prediction of cancer stage
252(2)
4.3 Influence of unlabeled data on the prediction of cancer stage for rare cancer types
254(1)
5 Conclusions
255(6)
Acknowledgments
256(1)
References
256(5)
Chapter 15 Applications of blockchain technology in smart healthcare: An overview
261(14)
Muhammad Hassan Nawaz
Muhammad Taimoor Khan
1 Introduction
261(3)
1.1 Comparison to other surveys
262(2)
2 Blockchain overview
264(1)
2.1 Key requirements
264(1)
3 Proposed healthcare monitoring framework
265(3)
4 Blockchain-enabled healthcare applications
268(3)
5 Potential challenges
271(1)
6 Concluding remarks
272(3)
References
272(3)
Chapter 16 Prediction of leukemia by classification and clustering techniques
275(22)
Kartik Rawal
Advika Parthvi
Dilip Kumar Choubey
Vaibhav Shukla
1 Introduction
275(1)
2 Motivation
276(1)
3 Literature review
276(6)
4 Description of proposed system
282(6)
4.1 Introduction and related concepts
282(1)
4.2 Framework for the proposed system
283(5)
5 Simulation results and discussion
288(5)
6 Conclusion and future directions
293(4)
References
293(4)
Chapter 17 Performance evaluation of fractal features toward seizure detection from electroencephalogram signals
297(14)
O.K. Fasil
R. Rajesh
1 Introduction
297(2)
2 Fractal dimension
299(1)
2.1 Katz fractal dimension
299(1)
2.2 Higuchi fractal dimension
299(1)
2.3 Petrosian fractal dimension
300(1)
3 Dataset
300(1)
4 Experiments
301(2)
5 Results and discussion
303(4)
6 Conclusion
307(4)
Acknowledgments
307(1)
References
307(4)
Chapter 18 Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences
311(16)
Sunil Datt Sharma
Pardeep Garg
1 Introduction
311(2)
2 Related work
313(1)
3 Algorithm for detection of TRs
314(3)
3.1 DNA sequences
314(1)
3.2 Numerical mapping
315(1)
3.3 Short time integer period discrete Fourier transform
315(1)
3.4 Thresholding
315(1)
3.5 Verification of the detected candidate TRs
316(1)
4 Performance analysis of the proposed algorithm
317(7)
5 Conclusion
324(3)
References
324(3)
Chapter 19 A blockchain solution for the privacy of patients' medical data
327(22)
Riya Sapra
Parneeta Dhaliwal
1 Introduction
327(1)
2 Stakeholders of healthcare industry
328(4)
2.1 Patients
330(1)
2.2 Pharmaceutical companies
330(1)
2.3 Healthcare providers (doctors, nurses, hospitals, nursing homes, clinics, etc.)
330(1)
2.4 Government
331(1)
2.5 Insurance companies
331(1)
3 Data protection laws for healthcare industry
332(1)
4 Medical data management
333(1)
5 Issues and challenges of healthcare industry
334(1)
6 Blockchain technology
335(5)
6.1 Features of blockchain
338(1)
6.2 Types of blockchain
338(2)
6.3 Working of blockchain
340(1)
7 Blockchain applications in healthcare
340(3)
8 Blockchain-based framework for privacy protection of patient's data
343(2)
9 Conclusion
345(4)
References
346(3)
Chapter 20 A novel approach for securing e-health application in a cloud environment
349(16)
Dipesh Kumar
Nirupama Mandal
Yugal Kumar
1 Introduction
349(2)
1.1 Contribution
351(1)
2 Motivation
351(2)
2.1 Related works
352(1)
2.2 Challenges
353(1)
3 Proposed system
353(7)
4 Conclusion
360(5)
References
362(3)
Chapter 21 An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
365(24)
Giuseppe Ciaburro
1 Introduction
366(1)
2 Data analytics
367(1)
3 Machine learning
368(1)
4 Approaching ensemble learning
369(2)
5 Understanding bagging
371(2)
6 Exploring boosting
373(1)
7 Discovering stacking
373(4)
7.1 Machine learning applications for healthcare analytics
374(1)
7.2 Machine learning-based model for disease diagnosis
374(1)
7.3 Machine learning-based algorithms to identify breast cancer
374(1)
7.4 Convolutional neural networks to detect cancer cells in brain images
375(1)
7.5 Machine learning techniques to detect prostate cancer in Magnetic resonance imaging
375(1)
7.6 Classification of respiratory diseases using machine learning
376(1)
7.7 Parkinson's disease diagnosis with machine learning-based models
376(1)
8 Processing drug discovery with machine learning
377(7)
8.1 Analyzing clinical data using machine learning algorithms
378(1)
8.2 Predicting thyroid disease using ensemble learning
378(1)
8.3 Machine learning-based applications for thyroid disease classification
379(1)
8.4 Preprocessing the dataset
380(2)
8.5 AdaBoostM algorithm
382(2)
9 Conclusion
384(5)
References
384(5)
Chapter 22 A review of deep learning models for medical diagnosis
389(16)
Seshadri Sastry Kunapuli
Praveen Chakravarthy Bhallamudi
1 Motivation
389(1)
2 Introduction
390(3)
3 MRI Segmentation
393(1)
4 Deep learning architectures used in diagnostic brain tumor analysis
394(4)
4.1 Convolutional neural networks or convnets
394(1)
4.2 Stacked autoencoders
394(1)
4.3 Deep belief networks
395(1)
4.4 2DU-Net
396(1)
4.5 3DU-Net
396(1)
4.6 Cascaded anisotropic network
397(1)
5 Deep learning tools applied to MRI images
398(1)
6 Proposed framework
399(1)
7 Conclusion and outlook
400(1)
8 Future directions
401(4)
References
401(4)
Chapter 23 Machine learning in precision medicine
405(16)
Dipankar Sengupta
1 Precision medicine
405(2)
2 Machine learning
407(1)
3 Machine learning in precision medicine
408(6)
3.1 Detection and diagnosis of a disease
410(2)
3.2 Prognosis of a disease
412(1)
3.3 Discovery of biomarkers and drug candidates
413(1)
4 Future opportunities
414(1)
5 Conclusions
415(6)
References
416(5)
Index 421
Dr. Pardeep Kumar is a Professor in the Department of Computer Science & Engineering at Jaypee University of Information Technology (JUIT), Wakanaghat. With more than 17 years of extensive experience in higher education, Dr. Kumar has served as Executive General Chair of 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) and 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC) , Guest Editor of Special Issue on "Robust and Secure Data Hiding Techniques for Telemedicine Applications", Multimedia Tools and Applications: An International Journal, Lead Guest Editor of Special Issue on "Recent Developments in Parallel, Distributed and Grid Computing for Big Data", published in International Journal of Grid and Utility Computing, Guest Editor of Special Issue on "Advanced Techniques in Multimedia Watermarking", published in International Journal of Information and Computer Security. Dr. Kumar is an Associate Editor of IEEE Access Journal. Dr. Kumars research focus includes machine & deep learning optimized Internet of Things (IOT) solutions to real life complex problems; blockchain, Internet of Things, data science and artificial intelligence for smart cities including AI driven health and medical informatics, big data analytics.

Dr. Kumar is an Associate Professor in the Department of Computer Engineering, School of Technology Management and Engineering, NMIMS University, Chandigarh Campus, Mumbai, India. Prior to joining NMIMS University, Dr. Kumar was associated with Jaypee University of Information Technology (JUIT), Wakanaghat, Himachal Pradesh, India. He completed his PhD in Computer Science & Engineering from Birla institute of Technology, Mesra, Ranchi. He has more than 17 years of teaching and research experience, has published over 120 research papers in reputed journals, edited more than eight books, and has presented at various national and international conferences. His primary area of research includes medical informatics, meta-heuristic algorithms, data clustering, swarm intelligence, pattern recognition, medical data analytics.

Mohamed A. Tawhid earned his PhD in Applied Mathematics from the University of Maryland Baltimore County, Maryland, United States. From 2000 to 2002, he was a postdoctoral fellow at the Faculty of Management, McGill University, Montreal, Quebec, Canada. Currently, he is a Professor at Thompson Rivers University, Kamloops, British Columbia, Canada. He has published more than 75 peer-reviewed research papers, 13 book chapters and edited four special issues in international journals. He has also co-authored a book published by Springer. His research has been funded by Natural Sciences and Engineering Research Council (NSERC) grants. Moreover, he has served on several journals' editorial boards and worked on several industrial projects in Canada. Fatos Xhafa, PhD in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016-2019). Prof. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters, edited books and proceedings in the field (H-index 55). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. Prof. Xhafa has an extensive editorial service. He is founder and Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index), and AE/EB Member of several indexed Int'l Journals. Prof. Xhafa is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things. His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at fatos@cs.upc.edu. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos