Оглавление
Группа авторов. Computational Intelligence and Healthcare Informatics
Table of Contents
List of Figures
List of Table
Guide
Pages
Computational Intelligence and Healthcare Informatics
Preface
1. Machine Learning and Big Data: An Approach Toward Better Healthcare Services
1.1 Introduction
1.2 Machine Learning in Healthcare
1.3 Machine Learning Algorithms
1.3.1 Supervised Learning
1.3.2 Unsupervised Learning
1.3.3 Semi-Supervised Learning
1.3.4 Reinforcement Learning
1.3.5 Deep Learning
1.4 Big Data in Healthcare
1.5 Application of Big Data in Healthcare. 1.5.1 Electronic Health Records
1.5.2 Helping in Diagnostics
1.5.3 Preventive Medicine
1.5.4 Precision Medicine
1.5.5 Medical Research
1.5.6 Cost Reduction
1.5.7 Population Health
1.5.8 Telemedicine
1.5.9 Equipment Maintenance
1.5.10 Improved Operational Efficiency
1.5.11 Outbreak Prediction
1.6 Challenges for Big Data
1.7 Conclusion
References
2. Thoracic Image Analysis Using Deep Learning
2.1 Introduction
2.2 Broad Overview of Research
2.2.1 Challenges
2.2.2 Performance Measuring Parameters
2.2.3 Availability of Datasets
2.3 Existing Models
2.4 Comparison of Existing Models
2.5 Summary
2.6 Conclusion and Future Scope
References
3. Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art
3.1 Introduction
3.1.1 Motivation of the Dimensionality Reduction
3.1.2 Feature Selection and Feature Extraction
3.1.3 Objectives of the Feature Selection
3.1.4 Feature Selection Process
3.2 Types of Feature Selection
3.2.1 Filter Methods
3.2.1.1 Correlation-Based Feature Selection
3.2.1.2 The Fast Correlation-Based Filter
3.2.1.3 The INTERACT Algorithm
3.2.1.4 ReliefF
3.2.1.5 Minimum Redundancy Maximum Relevance
3.2.2 Wrapper Methods
3.2.3 Embedded Methods
3.2.4 Hybrid Methods
3.3 Machine Learning and Deep Learning Models
3.3.1 Restricted Boltzmann Machine
3.3.2 Autoencoder
3.3.3 Convolutional Neural Networks
3.3.4 Recurrent Neural Network
3.4 Real-World Applications and Scenario of Feature Selection
3.4.1 Microarray
3.4.2 Intrusion Detection
3.4.3 Text Categorization
3.5 Conclusion
References
4. A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models
4.1 Introduction
4.2 Literature Review
4.3 Dataset, EDA, and Data Processing
4.4 Machine Learning Algorithms
4.4.1 Multinomial Naïve Bayes Classifier
4.4.2 Support Vector Machine Classifier
4.4.3 Random Forest Classifier
4.4.4 K-Nearest Neighbor Classifier
4.4.5 Decision Tree Classifier
4.4.6 Logistic Regression Classifier
4.4.7 Multilayer Perceptron Classifier
4.5 Work Architecture
4.6 Conclusion
References
5. Classification of Heart Sound Signals Using Time-Frequency Image Texture Features
5.1 Introduction
5.1.1 Motivation
5.2 Related Work
5.3 Theoretical Background
5.3.1 Pre-Processing Techniques
5.3.2 Spectrogram Generation
5.3.2 Feature Extraction
5.3.4 Feature Selection
5.3.5 Support Vector Machine
5.4 Proposed Algorithm
5.5 Experimental Results
5.5.1 Database
5.5.2 Evaluation Metrics
5.5.3 Confusion Matrix
5.5.4 Results and Discussions
5.6 Conclusion
References
6. Improving Multi-Label Classification in Prototype Selection Scenario
6.1 Introduction
6.2 Related Work
6.3 Methodology
Algorithm 6.1 Prototype selection based on the DBSCAN clustering algorithm
Algorithm 6.2 Prototype selection based on the SUBCLU clustering algorithm
6.3.1 Experiments and Evaluation
6.4 Performance Evaluation
6.5 Experiment Data Set
6.6 Experiment Results
6.7 Conclusion
References
7. A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease
7.1 Introduction
7.2 Materials and Methods
7.2.1 Dataset
7.2.2 Proposed Framework for Diabetes System
7.2.3 Pre-Processing of Data
7.3 Machine Learning Classification Hypotheses
7.3.1 K-Nearest Neighbor
7.3.2 Decision Tree
7.3.3 Random Forest
7.3.4 Logistic Regression
7.3.5 Naïve Bayes
7.3.6 Support Vector Machine
7.3.7 Adaptive Boosting
7.3.8 Extra-Tree Classifier
7.4 Classifier Validation Method
7.4.1 K-Fold Cross-Validation Technique
7.5 Performance Evaluation Metrics
7.6 Results and Discussion
7.6.1 Performance of All Classifiers Using 5-Fold CV Method
7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method
7.6.3 Performance of All Classifiers Using 10-Fold CV Method
7.7 Conclusion
References
8. Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease
8.1 Introduction
8.2 Related Work
8.3 Proposed Method
8.3.1 Dataset Description
8.3.2 Ensemble Learners for Classification Modeling
8.3.2.1 Bagging Ensemble Learners
Algorithm 8.1 Bagging ensemble learner
8.3.2.2 Boosting Ensemble Learner
Algorithm 8.2 AdaBoost learner
Algorithm 8.3 Gradient boosting learner
8.3.3 Hyperparameter Tuning of Ensemble Learners
8.3.3.1 Grid Search Algorithm
8.3.3.2 Random Search Algorithm
8.4 Experimental Outcomes and Analyses. 8.4.1 Characteristics of UCI Heart Disease Dataset
8.4.2 Experimental Result of Ensemble Learners and Performance Comparison
8.4.3 Analysis of Experimental Result
8.5 Conclusion
References
9. Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies
9.1 Introduction: Simulation in Healthcare
9.2 Need for a Healthcare Simulation Process
9.3 Types of Healthcare Simulations
9.4 AI in Healthcare Simulation
9.4.1 Machine Learning Models in Healthcare Simulation
9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction
9.4.1.1.1 High-Level Machine Learning Model Architecture
9.4.1.1.2 ML Methods, Evaluation, and Optimization
9.4.2 Deep Learning Models in Healthcare Simulation
9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model
9.4.2.1.1 Basic LSTM Unit
9.4.2.1.2 Bidirectional LSTM Model Network Architecture
9.5 Conclusion
References
10. Wolfram’s Cellular Automata Model in Health Informatics
10.1 Introduction
10.2 Cellular Automata
10.3 Application of Cellular Automata in Health Science
10.4 Cellular Automata in Health Informatics
10.5 Health Informatics–Deep Learning–Cellular Automata
10.6 Conclusion
References
11. COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques
11.1 Introduction
11.2 Literature Review
11.3 Data Pre-Processing
11.4 Proposed Methodologies
11.4.1 Simple Linear Regression
11.4.2 Association Rule Mining
Algorithm 11.1 Apriori method [26]
11.4.3 Back Propagation Neural Network
Algorithm 11.2 Levenberg Marquardt (LM) [4, 9]
11.5 Experimental Results
11.6 Conclusion and Future Scopes
References
12. Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach
12.1 Introduction
12.2 Literature Review
12.3 System Design
12.3.1 Extracting Feature With WMAR
12.4 Result and Discussion
12.5 Conclusion
References
13. Primary Healthcare Model for Remote Area Using Self-Organizing Map Network
13.1 Introduction
13.2 Background Details and Literature Review
13.2.1 Fuzzy Set
13.2.2 Self-Organizing Mapping
13.3 Methodology
13.3.1 Severity_Factor of Patient
13.3.2 Clustering by Self-Organizing Mapping
Algorithm 13.1 Self-Organizing Algorithm
13.4 Results and Discussion
13.5 Conclusion
References
14. Face Mask Detection in Real-Time Video Stream Using Deep Learning
14.1 Introduction
14.2 Related Work
14.3 Proposed Work
14.3.1 Dataset Description
14.3.2 Data Pre-Processing and Augmentation
14.3.3 VGG19 Architecture and Implementation
14.3.4 Face Mask Detection From Real-Time Video Stream
14.4 Results and Evaluation
14.5 Conclusion
References
15. A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms
15.1 Introduction
15.2 Research Problem Statements
15.3 Dataset Description
15.4 Machine Learning Technique Used for Skin Disease Identification
15.4.1 Logistic Regression
15.4.1.1 Logistic Regression Assumption
15.4.1.2 Logistic Sigmoid Function
15.4.1.3 Cost Function and Gradient Descent
15.4.2 SVM
15.4.3 Recurrent Neural Networks
15.4.4 Decision Tree Classification Algorithm
15.4.5 CNN
15.4.6 Random Forest
15.5 Result and Analysis
15.6 Conclusion
References
16. Asymptotic Patients’ Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario
16.1 Introduction
16.1.1 Motivation
16.1.2 Contributions
16.1.3 Paper Organization
16.1.4 System Model Problem Formulation
16.1.5 Proposed Methodology
16.2 Material Properties and Design Specifications. 16.2.1 Hardware Components. 16.2.1.1 Microcontroller
16.2.1.2 ESP8266 Wi-Fi Shield
16.2.2 Sensors. 16.2.2.1 Temperature Sensor (LM 35)
16.2.2.2 ECG Sensor (AD8232)
16.2.2.3 Pulse Sensor
16.2.2.4 GPS Module (NEO 6M V2)
16.2.2.5 Gyroscope (GY-521)
16.2.3 Software Components. 16.2.3.1 Arduino Software
16.2.3.2 MySQL Database
16.2.3.3 Wireless Communication
16.3 Experimental Methods and Materials. 16.3.1 Simulation Environment
16.3.1.1 System Hardware
16.3.1.2 Connection and Circuitry
16.3.1.2.1 Creation of Database and Website. 16.3.1.2.1.1 HOSTING PHP APPLICATION AND CREATION OF MYSQL DATABASE
16.3.1.2.1.2 CREATION OF API (APPLICATION PROGRAMMING INTERFACES) KEY
16.3.1.2.1.3 PREPARING MySQL DATABASE
16.3.1.2.1.4 CREATION AND INSERTION OF VALUES IN SQL TABLE
16.3.1.2.1.5 Adding Dynamic Graph to the Website
16.3.1.3 Protocols Used. 16.3.1.3.1 TCP/IP
16.3.1.3.2 UART
16.3.1.3.3 I2C Protocol
16.3.1.3.4 MQTT
16.3.1.4 Libraries Used. 16.3.1.4.1 ESP8266WiFi.h
16.3.1.4.2 ESP8266HTTPClient.h
16.3.1.4.3 Pulse-Sensor-Playground.h
16.3.1.4.4 Tinygpsplus.h
16.3.1.4.5 Adafruit-Sensor-Master
16.4 Simulation Results
16.5 Conclusion
16.6 Abbreviations and Acronyms
References
17. COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques
17.1 Introduction
17.2 Literature Survey
17.2.1 Cellular Automata
17.2.2 Image Segmentation
17.2.3 Deep Learning Techniques
17.3 Proposed Methodology
17.4 Results and Discussion
17.5 Conclusion
References
18. Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures
18.1 Introduction
18.2 Methods. 18.2.1 Data
18.3 GSA Model: Graph-Based Statistical Analysis
18.4 Graph-Based Analysis
18.4.1 Modeling Your Data as a Graph
18.4.2 RDF for Knowledge Graph
18.4.3 Knowledge Graph Representation
18.4.4 RDF Triple for KaTrace
18.4.5 Cipher Query Operation on Knowledge Graph
18.4.5.1 Inter-District Travel
18.4.5.2 Patient 653 Spread Analysis
18.4.5.3 Spread Analysis Using Parent-Child Relationships
18.4.5.4 Delhi Congregation Attended the Patient’s Analysis
18.5 Machine Learning Techniques
18.5.1 Apriori Algorithm
18.5.2 Decision Tree Classifier
18.5.3 System Generated Facts on Pandas
18.5.4 Time Series Model
18.6 Exploratory Data Analysis
18.6.1 Statistical Inference
18.7 Conclusion
18.8 Limitations
Acknowledgments
Abbreviations
References
19. Conceptualizing Tomorrow’s Healthcare Through Digitization
19.1 Introduction
19.2 Importance of IoMT in Healthcare
19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis. 19.3.1 Introduction to the Case Study
19.3.2 Merits
19.3.3 Proposed Design. 19.3.3.1 Homecare
19.3.3.2 Healthcare Provider
19.3.3.3 Community
19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea. 19.4.1 Introduction to the Case Study
19.4.2 Proposed Design
19.5 Future of Smart Healthcare
19.6 Conclusion
References
20. Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach
20.1 Introduction
20.1.1 COVID-19 Pandemic Situation
20.1.2 Salient Characteristics of Biomedical Corpus
20.2 Review of Related Literature. 20.2.1 Biomedical NLP Research
20.2.2 Domain Adaptation
20.2.3 POS Tagging in Hindi
20.3 Scope and Objectives. 20.3.1 Research Questions
20.3.2 Research Problem
20.3.3 Objectives
20.4 Methodological Design. 20.4.1 Method of Data Collection
20.4.2 Method of Data Annotation
20.4.2.1 The BIS Tagset
20.4.2.2 ILCI Semi-Automated Annotation Tool
20.4.2.3 IA Agreement
20.4.3 Method of Data Analysis
20.4.3.1 The Theory of Support Vector Machines
20.4.3.2 Experimental Setup
20.5 Evaluation
20.5.1 Error Analysis
20.5.2 Fleiss’ Kappa
20.6 Issues
20.7 Conclusion and Future Work
Acknowledgements
References
21. Application of Natural Language Processing in Healthcare
21.1 Introduction
21.2 Evolution of Natural Language Processing
21.3 Outline of NLP in Medical Management
21.4 Levels of Natural Language Processing in Healthcare
21.5 Opportunities and Challenges From a Clinical Perspective
21.5.1 Application of Natural Language Processing in the Field of Medical Health Records
21.5.2 Using Natural Language Processing for Large-Sample Clinical Research
21.6 Openings and Difficulties From a Natural Language Processing Point of View
21.6.1 Methods for Developing Shareable Data
21.6.2 Intrinsic Evaluation and Representation Levels
21.6.3 Beyond Electronic Health Record Data
21.7 Actionable Guidance and Directions for the Future
21.8 Conclusion
References
Index
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