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E-grāmata: Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions

Edited by , Edited by (Chitkara University, Department of Computer Science and Engineering, India), Edited by , Edited by (Chitkara University, Department of Computer Science and Engineering, India), Edited by (King Faisal University, College of Computer Sciences and Information Technology)
  • Formāts: EPUB+DRM
  • Sērija : Healthcare Technologies
  • Izdošanas datums: 24-Aug-2022
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839535123
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Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventionsPalielināt
  • Formāts: EPUB+DRM
  • Sērija : Healthcare Technologies
  • Izdošanas datums: 24-Aug-2022
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839535123
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A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs.

Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed.

This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.



This book examines machine learning trends in predictive technology to solve real-time healthcare problems. By using real-time data inputs to build predictive models, this new technology can model disease progression, assist with interventions or predict patient outcomes.

About the editors xv
1 COVID-19 detection in X-ray images using customized CNN model
1(20)
Deepshikha Jain
Venkatesh Gauri Shankar
Bali Devi
Surbhi Bhatia
1.1 Introduction
2(1)
1.2 Related work
3(3)
1.2.1 Key contributions and proposed work
5(1)
1.3 Materials and methods
6(5)
1.3.1 Feature extraction and selection
9(2)
1.4 Results and discussion
11(4)
1.5 Conclusion and future scope
15(6)
References
17(4)
2 Introducing deep learning in medical diagnosis
21(20)
N. Padmapriya
N. Kumaratharan
M. Pavithra
R. Rajmohan
P. Kanimozhi
D.J. Samuel
2.1 Introduction
22(1)
2.2 Literature survey
23(1)
2.3 Overview of DL algorithms
24(4)
2.3.1 Convolutional neural network
25(1)
2.3.2 Recurrent neural network
25(1)
2.3.3 Long short-term memory
26(1)
2.3.4 Restricted Boltzmann machine
27(1)
2.3.5 Deep belief networks
28(1)
2.4 Proposed DL framework for neuro disease diagnosis
28(4)
2.4.1 FAST-RCNN
29(2)
2.4.2 Ten fully connected layer
31(1)
2.5 Preprocessing of dataset
32(2)
2.6 Implementation and results
34(2)
2.7 Conclusion
36(5)
References
36(5)
3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML)
41(14)
Shubham Sharma
Pronika Chawla
Naincy Chamoli
Disha Pahuja
Maanya Mocha
3.1 Introduction
42(3)
3.1.1 DoS and DDoS attacks
43(1)
3.1.2 Man-in-the-middle (MitM) attack
44(1)
3.1.3 Phishing and spear-phishing attacks
44(1)
3.1.4 Password attack
44(1)
3.1.5 Eavesdropping attack
45(1)
3.1.6 Malware attack
45(1)
3.2 Related work
45(2)
3.3 Cloud computing
47(3)
3.3.1 Machine learning
47(1)
3.3.2 Exploratory data analysis
48(2)
3.4 Results
50(5)
References
54(1)
4 Classification methodologies in healthcare
55(20)
Adri Jovin John Joseph
Ferdin Joe John Joseph
Oswalt Manoj Stanislaus
Debashreet Das
4.1 Introduction
56(1)
4.2 Classification algorithms
57(3)
4.2.1 Statistical data
57(1)
4.2.2 Discriminant analysis
58(1)
4.2.3 Decision tree
58(1)
4.2.4 K-nearest neighbor (KNN)
59(1)
4.2.5 Logistic regression (LR)
59(1)
4.2.6 Bayesian classifier
59(1)
4.2.7 Support vector machine (SVM)
60(1)
4.3 Parameter identification
60(6)
4.3.1 Feature selection for classification
63(3)
4.4 Real-time applications
66(1)
4.4.1 Classification of patients based on medical record
66(1)
4.4.2 Predictive analytics and diagnostic analytics based on medical records
67(1)
44.3 Classification of diseases based on medical imaging
67(8)
4.4.4 Mixed reality-based automation to help aid aging society
68(1)
4.4.5 Tiny ML-based classification systems for medical gadgets
69(1)
4.4.6 Classification systems for insurance claim management
69(1)
4.4.7 Case study: Inspectra from Perceptra
70(1)
4.4.8 Deep learning for beginners
71(1)
References
72(3)
5 Introducing deep learning in medical domain
75(18)
Prithi Santuel
Anusha Bamini
P. Nancy
S. Oswalt Manoj
Maruti Perumal
5.1 Introduction
76(5)
5.1.1 DL in a nutshell
77(1)
5.1.2 History of DL in the medical field
77(2)
5.1.3 Benefits of DL in the medical domain
79(1)
5.1.4 Challenges and obstacles of DL in the medical domain
80(1)
5.1.5 Opportunities of DL in the medical field
81(1)
5.2 DL applications in the medical domain
81(3)
5.2.1 Drug discovery and medicine precision
81(1)
5.2.2 Detection of diseases
82(1)
5.2.3 Diagnosing patients
83(1)
5.2.4 Healthcare administration
83(1)
5.3 DL for medical image analysis
84(5)
5.3.1 Medical image detection
85(1)
5.3.2 Medical image recognition
86(1)
5.3.3 Medical image segmentation
87(1)
5.3.4 Medical image registration
88(1)
5.3.5 Disease diagnosis and quantification
89(1)
5.4 Conclusion
89(4)
References
90(3)
6 Deep-stacked autoencoder for medical image classification
93(24)
J. Anitha
S. Akila Agnes
S. Immanuel Alex Pandian
Malin Bruntha
6.1 Introduction
93(3)
6.2 Autoencoder
96(5)
6.2.1 Stacked AE
97(1)
6.2.2 Sparse AE
97(3)
6.2.3 Convolutional AE
100(1)
6.2.4 DeepAE
101(1)
6.3 Proposed method
101(3)
6.3.1 Representation learning using AE
102(1)
6.3.2 Softmax layer
102(1)
6.3.3 Support vector machine
103(1)
6.3.4 K-nearest neighbor
103(1)
6.3.5 Fine-tuning
104(1)
6.3.6 Sparsity and regularization in AE
104(1)
6.4 Results and discussions
104(9)
6.4.1 Datasets
104(1)
6.4.2 Evaluation metrics
105(1)
6.4.3 Analysis of the simple AE
106(3)
6.4.4 Effect of sparsity in AE
109(1)
6.4.5 Effect of squeezing bottleneck in AE
110(1)
6.4.6 Performance of deep stacked encoder
111(2)
6.5 Conclusion
113(4)
References
113(4)
7 Comparison of machine learning and deep learning algorithms for prediction of coronary heart disease
117(26)
Sajeev Ram Arumugam
E. Anna Devi
T. Abimala
Oswalt Manoj
7.1 Introduction
118(1)
7.1.1 Coronary heart disease (CHD)
118(1)
7.1.2 ML and DL techniques
118(1)
7.2 Related works
119(2)
7.3 Materials and methods
121(14)
7.3.1 Data preparation
121(1)
7.3.2 Fixing the missing data issue
122(2)
7.3.3 Data analysis
124(2)
7.3.4 Feature selection
126(1)
7.3.5 Balancing the dataset
127(1)
7.3.6 Feature scaling
128(1)
7.3.7 Methodology
129(5)
7.3.8 Performance metrics
134(1)
7.4 Results and discussion
135(5)
7.5 Conclusion
140(3)
References
140(3)
8 Revolution in technology-enabled healthcare: Internet of Things
143(20)
S. Sangeetha
Deepa Shanmugham
S. Balamurugan
K. Maharaja
8.1 IoT and healthcare information systems
144(1)
8.2 Remote health monitoring and telehealth
145(3)
8.2.1 PharmaloT
146(1)
8.2.2 Mobile applications for healthcare
147(1)
8.2.3 Big data in healthcare
147(1)
8.2.4 Challenges in MIoT
148(1)
8.3 Wearables and medical devices
148(2)
8.3.1 Activity trackers
148(1)
8.3.2 Vital sign measurement
149(1)
8.3.3 Smart jacket
149(1)
8.3.4 Wire-based wearable devices
150(1)
8.4 IoT in chronic diseases
150(3)
8.5 IoT in emergency medical care
153(1)
8.6 IoT and pregnancy care
154(1)
8.7 IoT in eyecare
155(2)
8.7.1 Visual acuity tester
155(1)
8.7.2 Mobile imaging
156(1)
8.8 Benefits of IoT in the healthcare system
157(1)
8.9 Challenges with IoT in healthcare
158(5)
References
159(4)
9 Smart healthcare monitoring framework using IoT with big data analytics
163(22)
S. Usharani
P. Manju Bala
T. Ananth Kumar
R. Rajmohan
A. Balachandar
A.S. Adeola
9.1 Introduction
164(1)
9.2 Related work
165(1)
9.3 Overview of IoT and big data
165(1)
9.4 Data sources for healthcare
166(2)
9.4.1 Electronic health records (EHR) data
167(1)
9.4.2 Medical images data
167(1)
9.4.3 Experimental data mining
167(1)
9.4.4 Interactive data
168(1)
9.4.5 Genomic data
168(1)
9.5 Big data's evolution in IoT
168(1)
9.6 Recent trends in big data analytics and IoT
169(2)
9.6.1 Specialized medical envisioning
169(1)
9.6.2 Telehealth
169(1)
9.6.3 Portable gadgets and the IoT
170(1)
9.6.4 Biological IoT
170(1)
9.7 Big data challenges in healthcare
171(1)
9.7.1 Challenges relating to budgetary and economic considerations
171(1)
9.7.2 Challenges relating to expertise
171(1)
9.8 IoT challenges in healthcare
172(8)
9.8.1 IoT and portable gadgets
172(1)
9.8.2 Modes of communication in wearable devices
173(1)
9.8.3 Smart healthcare monitoring frameworks
174(1)
9.8.4 SHMS principles in the IoT
175(1)
9.8.5 Implementation of SHMS with big data analytics
176(1)
9.8.6 Proposed model
176(1)
9.8.7 Case study
177(1)
9.8.8 Performance evaluation of data analysis
177(3)
9.9 Conclusion
180(5)
References
181(4)
10 Experimental analysis and investigation of dementia detection framework using EHR-based variant LSTM model
185(22)
P. Manju Bala
S. Usharani
R. Rajmohan
T. Ananth Kumar
A. Balachandar
S. Arunmozhi Selvi
10.1 Introduction
186(1)
10.2 Related work
187(1)
10.3 Materials and methods
188(7)
10.3.1 EHRdatasets
188(1)
10.3.2 ML models
189(1)
10.3.3 Approach to deep learning
190(1)
10.3.4 Analysis of models
191(2)
10.3.5 Proposed methodology
193(1)
10.3.6 Model architecture
193(2)
10.4 Dataset for the suggested method
195(2)
10.4.1 Dataset pre-processing
195(1)
10.4.2 Parameters of the CNN model
196(1)
10.4.3 Parameters of the RNN model
196(1)
10.4.4 Parameters of the LSTM model
197(1)
10.5 Dementia detection and prediction model
197(2)
10.6 Experimental results
199(4)
10.7 Conclusion
203(4)
References
204(3)
11 An intelligent agent-based distributed patient scheduling using token-based coordination approach: a case study
207(20)
E. Grace Mary Kanaga
M.L. Valarmathi
J. Dhiviya Rose
Lincy Grace Kanagaselvi
11.1 Introduction
208(3)
11.1.1 Brief introduction to agent paradigm
208(1)
11.1.2 Patient scheduling
209(1)
11.1.3 Agent-based patient scheduling
210(1)
11.2 Context of study and problem description
211(3)
11.2.1 Application of agents in healthcare
212(1)
11.2.2 Application of agents in scheduling
213(1)
11.2.3 MAS toward coordination
213(1)
11.3 Related work
214(3)
11.3.1 Token as a coordination mechanism
214(1)
11.3.2 Agent-based patient scheduling using token-based coordination
215(1)
11.3.3 Algorithm for updating the nonlocal viewpoints of the resource
216(1)
11.4 Model implementation and validation
217(4)
11.4.1 Performance metrics
217(1)
11.4.2 Comparison of results
217(4)
11.5 Conclusion
221(6)
References
222(5)
12 Internet of Things (IoT) for the efficient healthcare system
227(16)
Abhishek Choubey
Shruti Bhargava Choubey
12.1 Introduction
227(2)
12.2 Overview of IoT
229(3)
12.3 Review of existing work
232(3)
12.4 IoT architecture for Chikungunya and COVID-19
235(8)
13 Comprehension of melody representation and speed-up approaches for query by humming system
243(18)
C.N. Trisiladevi
P. Mahesha
13.1 Introduction
244(1)
13.2 Comparison with existing approaches
245(1)
13.3 Experimental analysis of the proposed work
246(9)
13.3.1 Mean reciprocal rank
246(5)
13.3.2 Mean of average
251(2)
13.3.3 Top X hit rate
253(1)
13.3.4 Retrieval time
253(2)
13.4 Approximation and envisioning of relations among performance appraisal metrics
255(3)
13.4.1 Relevance analysis of mean reciprocal and mean of average rank
255(1)
13.4.2 Synchronisation of accuracy and retrieval time with intersection point analysis
256(2)
13.5 Conclusion
258(3)
References
258(3)
14 Python for digital health solutions: elevated outcomes
261(18)
Ayushmaan Khurana
Mayank Aggarwal
14.1 Introduction
261(1)
14.2 An overview of the evolution of the healthcare industry
262(1)
14.2.1 A case study of Singapore
262(1)
14.3 Python's role in the healthcare industry
263(11)
14.3.1 Healthcare data management
264(2)
14.3.2 Healthcare simulations
266(4)
14.3.3 Medical diagnosis, prognosis and treatment
270(2)
14.3.4 Genomics and sequencing
272(1)
14.3.5 A double-edged sword: the disadvantages of Python's implementation
273(1)
14.4 Conclusion
274(5)
Glossary
274(1)
References
275(4)
15 IoT-enabled healthcare - a paradigm shift
279(16)
Parul Gandhi
Preeti Chikara
Meenakshi Yadav
15.1 Introduction
279(1)
15.2 Architecture of IoT
280(3)
15.3 IoT implementation in medical field
283(4)
15.3.1 Architecture of medical IoT (MIoT)
283(1)
15.3.2 Types of sensors used in MIoT
284(1)
15.3.3 Tools and technologies used to implement MIoT
285(1)
15.3.4 Functioning of healthcare system
286(1)
15.4 IoT-enabled devices in healthcare
287(1)
15.5 IoT technologies in medical field
288(3)
15.6 Security challenges
291(1)
15.6.1 Privacy and security
292(1)
15.6.2 Data overloaded and accuracy
292(1)
15.6.3 Outdated infrastructure
292(1)
15.6.4 Cyber attack
292(1)
15.7 Conclusion
292(3)
References
293(2)
16 IoT-based cardiovascular prediction framework using deep learning algorithms
295(26)
A. Devi
S. Matilda
G. Kavya
T. Ananth Kumar
G. Glorindal
16.1 Introduction
295(3)
16.1.1 Different types of CVDs
296(1)
16.1.2 Intermediate risk factors of CVDs
297(1)
16.1.3 Symptoms and prevention of CVDs
297(1)
16.2 Related works
298(3)
16.3 Introduction to deep learning
301(3)
16.3.1 Deep learning vs. machine learning
301(1)
16.3.2 Workflow of deep learning
302(1)
16.3.3 Type of deep learning networks or algorithms
302(2)
16.4 Proposed framework
304(6)
16.4.1 Objectives of the proposed framework
304(1)
16.4.2 Proposed framework
304(1)
16.4.3 Methodologies
304(6)
16.5 Discussion on experimental results
310(7)
16.5.1 Hardware description
310(1)
16.5.2 Dataset description
310(2)
16.5.3 Selected features and evaluation parameters
312(1)
16.5.4 Simulation results
313(4)
16.6 Conclusion and future enhancement
317(4)
References
317(4)
17 An intelligent approach using convolutional neural network (CNN) for early detection of melanoma and other skin diseases
321(30)
Sharmila Banu Sheik Imam
Preethika Immaculate Britto
17.1 Introduction
322(3)
17.1.1 The skin
322(1)
17.1.2 Anatomy of the skin
322(1)
17.1.3 Problem statement
322(3)
17.2 Scope of the project
325(7)
17.2.1 Comprehensive analysis of related work
325(2)
17.2.2 Dermatological disease detection using image processing and artificial neural network
327(1)
17.2.3 Automatic detection and severity measurement of eczema using image processing
328(2)
17.2.4 Skin cancer classification using deep learning and transfer learning
330(1)
17.2.5 Dermatol ogical classification using deep learning of skin image and patient background knowledge
331(1)
17.3 Project requirements
332(2)
17.3.1 Functional requirements
332(2)
17.3.2 Non-functional requirements
334(1)
17.3.3 Software requirements
334(1)
17.4 Identification of alternative solutions and justification of selecting a solution
334(2)
17.4.1 Acquisition of image
334(1)
17.4.2 Classification types
335(1)
17.4.3 CNN pre-trained model
335(1)
17.4.4 Pre-processing of image
336(1)
17.5 Application analysis
336(2)
17.5.1 Model block diagram
336(1)
17.5.2 Flowchart diagram
336(1)
17.5.3 Use-case diagram
337(1)
17.6 Details of the project implementation conforming to the proposal phase
338(10)
17.6.1 Android mobile application front-end
338(2)
17.6.2 Mobile application back-end development
340(4)
17.6.3 Data preparation
344(1)
17.6.4 Image processing for hair removal
344(1)
17.6.5 Classification model building and training
345(3)
17.7 Conclusion and future work
348(3)
References
348(3)
18 Self-organizing deep learning approach for controlling movements of wheeled apparatus through corneal connotation
351(10)
Shubhangi Rathkanthiwar
18.1 Introduction
351(2)
18.2 Previous works
353(2)
18.3 Methodology
355(1)
18.4 Structural details
355(4)
18.5 Conclusion
359(2)
References
360(1)
19 Prediction of breast tumour outcome to chemotherapy using statistical MR images through deep learning approaches
361(18)
Md. Khaja Mohiddin
Neha Mehta
V.B.S. Srilatha Indira Dutt
Seira Shinde
Amit Thakur
G. Anand Kumar
19.1 Introduction
362(2)
19.2 Materials and methods
364(2)
19.2.1 Dataset
364(1)
19.2.2 Neoadjuvant chemotherapy
364(1)
19.2.3 MRI acquisition and parameters
364(1)
19.2.4 Image processing
365(1)
19.2.5 Data augmentation
365(1)
19.3 CNN architectures
366(2)
19.3.1 Single-input architecture
366(1)
19.3.2 Multiple inputs architecture
367(1)
19.4 Method evaluations
368(1)
19.5 Results and discussion
368(4)
19.6 Conclusion and future scope
372(7)
References
372(7)
20 Risk analysis and prediction of cancer associated with Type II diabetes: a review
379(12)
K.N. Soumya
P. Vigneshwaran
20.1 Introduction
379(1)
20.2 Diabetes
380(1)
20.2.1 Type I diabetes
380(1)
20.2.2 Type II diabetes
380(1)
20.3 Cancer
380(1)
20.4 Related works
381(3)
20.5 Performance analysis of existing methods
384(1)
20.6 Conclusion and future work
385(6)
References
386(5)
Index 391
Abhishek Kumar is an assistant professor in the Department of Computer Science and Engineering at the Institute of Engineering and Technology, Chitkara University, India. He has a Doctorate in Computer Science from the University of Madras and is a Post-Doctoral Fellow of the Ingenium Research Group, Universidad De Castilla-La Mancha, Spain. He has published over 80 journal papers and edited 21 books. His research areas include artificial intelligence, image processing, computer vision, data mining, and machine learning.



Ashutosh Kumar Dubey is an associate professor in the Department of Computer Science and Engineering at the Institute of Engineering and Technology, Chitkara University, India. He is a Senior Member of IEEE and ACM. His research interests include machine learning, health informatics, and cloud computing.



Surbhi Bhatia is an associate professor at the College of Computer Sciences and Information Technology, King Faisal University, Saudi Arabia. She is PMP certified from PMI, USA. She has published 75 papers in reputed journals and conferences and edited 9 books. She has 9 patents from the USA, Australia, and India and has completed 5 research projects from the Ministry of Education and DSR, Saudi Arabia. Her research interests include sentiment analysis, data analytics, and machine learning.



Swarn Avinash Kumar is an applied research scientist at Meta Platforms, Inc. (previously Facebook), USA. He has previously worked in software engineering roles at Lyft Inc., Google and Amazon. He has filed multiple patents in the autonomous industry and the machine learning sphere. His research interests include robotics, deep learning, natural language processing, and computer vision.



Dac-Nhuong Le is an associate professor in the Department of Computer Science and head of the Faculty of Information Technology at Haiphong University, Vietnam. He has more than 20 years of academic teaching experience and has published more than 80 papers, presentations, and book chapters. His research interests include evolutionary multi-objective optimization, network communication and security, cloud computing, and virtual/argument reality.
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