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

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Contents
About the editors
1 COVID-19 detection in X-ray images using customized CNN model
1.1 Introduction
1.2 Related work
1.2.1 Key contributions and proposed work
1.3 Materials and methods
1.3.1 Feature extraction and selection
1.4 Results and discussion
1.5 Conclusion and future scope
References
2 Introducing deep learning in medical diagnosis
2.1 Introduction
2.2 Literature survey
2.3 Overview of DL algorithms
2.3.1 Convolutional neural network
2.3.2 Recurrent neural network
2.3.3 Long short-term memory
2.3.4 Restricted Boltzmann machine
2.3.5 Deep belief networks
2.4 Proposed DL framework for neuro disease diagnosis
2.4.1 FAST-RCNN
2.4.2 Ten fully connected layer
2.5 Preprocessing of dataset
2.6 Implementation and results
2.7 Conclusion
References
3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML)
3.1 Introduction
3.1.1 DoS and DDoS attacks
3.1.2 Man-in-the-middle (MitM) attack
3.1.3 Phishing and spear-phishing attacks
3.1.4 Password attack
3.1.5 Eavesdropping attack
3.1.6 Malware attack
3.2 Related work
3.3 Cloud computing
3.3.1 Machine learning
3.3.2 Exploratory data analysis
3.4 Results
References
4 Classification methodologies in healthcare
4.1 Introduction
4.2 Classification algorithms
4.2.1 Statistical data
4.2.2 Discriminant analysis
4.2.3 Decision tree
4.2.4 K-nearest neighbor (KNN)
4.2.5 Logistic regression (LR)
4.2.6 Bayesian classifier
4.2.7 Support vector machine (SVM)
4.3 Parameter identification
4.3.1 Feature selection for classification
4.4 Real-time applications
4.4.1 Classification of patients based on medical record
4.4.2 Predictive analytics and diagnostic analytics based on medical records
4.4.3 Classification of diseases based on medical imaging
4.4.4 Mixed reality-based automation to help aid aging society
4.4.5 Tiny ML-based classification systems for medical gadgets
4.4.6 Classification systems for insurance claim management
4.4.7 Case study: Inspectra from Perceptra
4.4.8 Deep learning for beginners
References
5 Introducing deep learning in medical domain
5.1 Introduction
5.1.1 DL in a nutshell
5.1.2 History of DL in the medical field
5.1.3 Benefits of DL in the medical domain
5.1.4 Challenges and obstacles of DL in the medical domain
5.1.5 Opportunities of DL in the medical field
5.2 DL applications in the medical domain
5.2.1 Drug discovery and medicine precision
5.2.2 Detection of diseases
5.2.3 Diagnosing patients
5.2.4 Healthcare administration
5.3 DL for medical image analysis
5.3.1 Medical image detection
5.3.2 Medical image recognition
5.3.3 Medical image segmentation
5.3.4 Medical image registration
5.3.5 Disease diagnosis and quantification
5.4 Conclusion
References
6 Deep-stacked autoencoder for medical image classification
6.1 Introduction
6.2 Autoencoder
6.2.1 Stacked AE
6.2.2 Sparse AE
6.2.3 Convolutional AE
6.2.4 Deep AE
6.3 Proposed method
6.3.1 Representation learning using AE
6.3.2 Softmax layer
6.3.3 Support vector machine
6.3.4 K-nearest neighbor
6.3.5 Fine-tuning
6.3.6 Sparsity and regularization in AE
6.4 Results and discussions
6.4.1 Datasets
6.4.2 Evaluation metrics
6.4.3 Analysis of the simple AE
6.4.4 Effect of sparsity in AE
6.4.5 Effect of squeezing bottleneck in AE
6.4.6 Performance of deep stacked encoder
6.5 Conclusion
References
7 Comparison of machine learning and deep learning algorithms for prediction of coronary heart disease
7.1 Introduction
7.1.1 Coronary heart disease (CHD)
7.1.2 ML and DL techniques
7.2 Related works
7.3 Materials and methods
7.3.1 Data preparation
7.3.2 Fixing the missing data issue
7.3.3 Data analysis
7.3.4 Feature selection
7.3.5 Balancing the dataset
7.3.6 Feature scaling
7.3.7 Methodology
7.3.8 Performance metrics
7.4 Results and discussion
7.5 Conclusion
References
8 Revolution in technology-enabled healthcare: Internet of Things
8.1 IoT and healthcare information systems
8.2 Remote health monitoring and telehealth
8.2.1 PharmaIoT
8.2.2 Mobile applications for healthcare
8.2.3 Big data in healthcare
8.2.4 Challenges in MIoT
8.3 Wearables and medical devices
8.3.1 Activity trackers
8.3.2 Vital sign measurement
8.3.3 Smart jacket
8.3.4 Wire-based wearable devices
8.4 IoT in chronic diseases
8.5 IoT in emergency medical care
8.6 IoT and pregnancy care
8.7 IoT in eyecare
8.7.1 Visual acuity tester
8.7.2 Mobile imaging
8.8 Benefits of IoT in the healthcare system
8.9 Challenges with IoT in healthcare
References
9 Smart healthcare monitoring framework using IoT with big data analytics
9.1 Introduction
9.2 Related work
9.3 Overview of IoT and big data
9.4 Data sources for healthcare
9.4.1 Electronic health records (EHR) data
9.4.2 Medical images data
9.4.3 Experimental data mining
9.4.4 Interactive data
9.4.5 Genomic data
9.5 Big data’s evolution in IoT
9.6 Recent trends in big data analytics and IoT
9.6.1 Specialized medical envisioning
9.6.2 Telehealth
9.6.3 Portable gadgets and the IoT
9.6.4 Biological IoT
9.7 Big data challenges in healthcare
9.7.1 Challenges relating to budgetary and economic considerations
9.7.2 Challenges relating to expertise
9.8 IoT challenges in healthcare
9.8.1 IoT and portable gadgets
9.8.2 Modes of communication in wearable devices
9.8.3 Smart healthcare monitoring frameworks
9.8.4 SHMS principles in the IoT
9.8.5 Implementation of SHMS with big data analytics
9.8.6 Proposed model
9.8.7 Case study
9.8.8 Performance evaluation of data analysis
9.9 Conclusion
References
10 Experimental analysis and investigation of dementia detection framework using EHR-based variant LSTM model
10.1 Introduction
10.2 Related work
10.3 Materials and methods
10.3.1 EHR datasets
10.3.2 ML models
10.3.3 Approach to deep learning
10.3.4 Analysis of models
10.3.5 Proposed methodology
10.3.6 Model architecture
10.4 Dataset for the suggested method
10.4.1 Dataset pre-processing
10.4.2 Parameters of the CNN model
10.4.3 Parameters of the RNN model
10.4.4 Parameters of the LSTM model
10.5 Dementia detection and prediction model
10.6 Experimental results
10.7 Conclusion
References
11 An intelligent agent-based distributed patient scheduling using token-based coordination approach: a case study
11.1 Introduction
11.1.1 Brief introduction to agent paradigm
11.1.2 Patient scheduling
11.1.3 Agent-based patient scheduling
11.2 Context of study and problem description
11.2.1 Application of agents in healthcare
11.2.2 Application of agents in scheduling
11.2.3 MAS toward coordination
11.3 Related work
11.3.1 Token as a coordination mechanism
11.3.2 Agent-based patient scheduling using token-based coordination
11.3.3 Algorithm for updating the nonlocal viewpoints of the resource
11.4 Model implementation and validation
11.4.1 Performance metrics
11.4.2 Comparison of results
11.5 Conclusion
References
12 Internet of Things (IoT) for the efficient healthcare system
12.1 Introduction
12.2 Overview of IoT
12.3 Review of existing work
12.4 IoT architecture for Chikungunya and COVID-19
12.5 Conclusions and future scope
References
13 Comprehension of melody representation and speed-up approaches for query by humming system
13.1 Introduction
13.2 Comparison with existing approaches
13.3 Experimental analysis of the proposed work
13.3.1 Mean reciprocal rank
13.3.2 Mean of average
13.3.3 Top X hit rate
13.3.4 Retrieval time
13.4 Approximation and envisioning of relations among performance appraisal metrics
13.4.1 Relevance analysis of mean reciprocal and mean of average rank
13.4.2 Synchronisation of accuracy and retrieval time with intersection point analysis
13.5 Conclusion
References
14 Python for digital health solutions: elevated outcomes
14.1 Introduction
14.2 An overview of the evolution of the healthcare industry
14.2.1 A case study of Singapore
14.3 Python’s role in the healthcare industry
14.3.1 Healthcare data management
14.3.2 Healthcare simulations
14.3.3 Medical diagnosis, prognosis and treatment
14.3.4 Genomics and sequencing
14.3.5 A double-edged sword: the disadvantages of Python’s implementation
14.4 Conclusion
Glossary
References
15 IoT-enabled healthcare – a paradigm shift
15.1 Introduction
15.2 Architecture of IoT
15.3 IoT implementation in medical field
15.3.1 Architecture of medical IoT (MIoT)
15.3.2 Types of sensors used in MIoT
15.3.3 Tools and technologies used to implement MIoT
15.3.4 Functioning of healthcare system
15.4 IoT-enabled devices in healthcare
15.5 IoT technologies in medical field
15.6 Security challenges
15.6.1 Privacy and security
15.6.2 Data overloaded and accuracy
15.6.3 Outdated infrastructure
15.6.4 Cyber attack
15.7 Conclusion
References
16 IoT-based cardiovascular prediction framework using deep learning algorithms
16.1 Introduction
16.1.1 Different types of CVDs
16.1.2 Intermediate risk factors of CVDs
16.1.3 Symptoms and prevention of CVDs
16.2 Related works
16.3 Introduction to deep learning
16.3.1 Deep learning vs. machine learning
16.3.2 Workflow of deep learning
16.3.3 Type of deep learning networks or algorithms
16.4 Proposed framework
16.4.1 Objectives of the proposed framework
16.4.2 Proposed framework
16.4.3 Methodologies
16.5 Discussion on experimental results
16.5.1 Hardware description
16.5.2 Dataset description
16.5.3 Selected features and evaluation parameters
16.5.4 Simulation results
16.6 Conclusion and future enhancement
References
17 An intelligent approach using convolutional neural network (CNN) for early detection of melanoma and other skin diseases
17.1 Introduction
17.1.1 The skin
17.1.2 Anatomy of the skin
17.1.3 Problem statement
17.2 Scope of the project
17.2.1 Comprehensive analysis of related work
17.2.2 Dermatological disease detection using image processing and artificial neural network
17.2.3 Automatic detection and severity measurement of eczema using image processing
17.2.4 Skin cancer classification using deep learning and transfer learning
17.2.5 Dermatological classification using deep learning of skin image and patient background knowledge
17.3 Project requirements
17.3.1 Functional requirements
17.3.2 Non-functional requirements
17.3.3 Software requirements
17.4 Identification of alternative solutions and justification of selecting a solution
17.4.1 Acquisition of image
17.4.2 Classification types
17.4.3 CNN pre-trained model
17.4.4 Pre-processing of image
17.5 Application analysis
17.5.1 Model block diagram
17.5.2 Flowchart diagram
17.5.3 Use-case diagram
17.6 Details of the project implementation conforming to the proposal phase
17.6.1 Android mobile application front-end
17.6.2 Mobile application back-end development
17.6.3 Data preparation
17.6.4 Image processing for hair removal
17.6.5 Classification model building and training
17.7 Conclusion and future work
References
18 Self-organizing deep learning approach for controlling movements of wheeled apparatus through corneal connotation
18.1 Introduction
18.2 Previous works
18.3 Methodology
18.4 Structural details
18.5 Conclusion
References
19 Prediction of breast tumour outcome to chemotherapy using statistical MR images through deep learning approaches
19.1 Introduction
19.2 Materials and methods
19.2.1 Dataset
19.2.2 Neoadjuvant chemotherapy
19.2.3 MRI acquisition and parameters
19.2.4 Image processing
19.2.5 Data augmentation
19.3 CNN architectures
19.3.1 Single-input architecture
19.3.2 Multiple inputs architecture
19.4 Method evaluations
19.5 Results and discussion
19.6 Conclusion and future scope
References
20 Risk analysis and prediction of cancer associated with Type II diabetes: a review
20.1 Introduction
20.2 Diabetes
20.2.1 Type I diabetes
20.2.2 Type II diabetes
20.3 Cancer
20.4 Related works
20.5 Performance analysis of existing methods
20.6 Conclusion and future work
References
Index
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