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Submit a ProposalComputational Intelligence and Healthcare Informatics
 | Edited by Om Prakash Jena, Alok Ranjan Tripathy, Ahmed A. Elngar and Zdzislaw Polkowski
Series: Machine Learning in Biomedical Science and Healthcare Informatics Copyright: 2021 | Status: Published ISBN: 9781119818687 | Hardcover | 432 pages | 197 illustrations Price: $225 USD  |
One Line Description
The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis.
Audience
The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.
The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.
Description
Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments.
This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis.
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Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments.
This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis.
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Author / Editor Details
Om Prakash Jena, PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. He has more than 30 research articles in peer-reviewed journals and 4 patents.
Om Prakash Jena, PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. He has more than 30 research articles in peer-reviewed journals and 4 patents.
Alok Ranjan Tripathy, PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, and Odisha, India.
Ahmed A. Elngar, PhD is an assistant professor of Computer Science, Chair of Scientific Innovation Research Group (SIRG), and Director of Technological and Informatics Studies Center, at Beni-Suef University, Egypt.
Zdzislaw Polkowski, PhD is a professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals.
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Table of Contents
Preface
Part I: Introduction
1. Machine Learning and Big Data: An Approach Toward Better Healthcare Services
Nahid Sami and Asfia Aziz
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
Part II: Medical Data Processing and Analysis
2. Thoracic Image Analysis Using Deep Learning
Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi
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
G. Manikandan and S. Abirami
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
Parvej Reja Saleh and Eeshankur Saikia
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
Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar
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
Himanshu Suyal and Avtar Singh
6.1 Introduction
6.2 Related Work
6.3 Methodology
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
Maqsood Hayat, Yar Muhammad and Muhammad Tahir
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
Dhilsath Fathima M. and S. Justin Samuel
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
8.3.2.2 Boosting Ensemble 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
Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj
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
9.4.2 Deep Learning Models in Healthcare Simulation
9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model
9.5 Conclusion
References
10. Wolfram’s Cellular Automata Model in Health Informatics
Sutapa Sarkar and Mousumi Saha
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
Part III: Machine Learning and COVID Prospective
11. COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques
Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena
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
11.4.3 Back Propagation Neural Network
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
Sivanantham Kalimuthu
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
Sayan Das and Jaya Sil
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
13.4 Results and Discussion
13.5 Conclusion
References
14. Face Mask Detection in Real-Time Video Stream Using Deep Learning
Alok Negi and Krishan Kumar
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
Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar
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
Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari
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 Microcontrolle
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
16.3.1.3 Protocols Used
16.3.1.4 Libraries Used
16.4 Simulation Results
16.5 Conclusion
16.6 Abbreviations and Acronyms
References
17. COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques
Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik
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
Abhilash C. B. and Kavi Mahesh
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 Congeegation 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
Part IV: Prospective of Computational Intelligence in Healthcare
19. Conceptualizing Tomorrow’s Healthcare Through Digitization
Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena
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 363 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
Pitambar Behera and Om Prakash Jena
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 Target
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
Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas
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
Back to Top
Preface
Part I: Introduction
1. Machine Learning and Big Data: An Approach Toward Better Healthcare Services
Nahid Sami and Asfia Aziz
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
Part II: Medical Data Processing and Analysis
2. Thoracic Image Analysis Using Deep Learning
Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi
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
G. Manikandan and S. Abirami
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
Parvej Reja Saleh and Eeshankur Saikia
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
Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar
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
Himanshu Suyal and Avtar Singh
6.1 Introduction
6.2 Related Work
6.3 Methodology
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
Maqsood Hayat, Yar Muhammad and Muhammad Tahir
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
Dhilsath Fathima M. and S. Justin Samuel
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
8.3.2.2 Boosting Ensemble 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
Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj
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
9.4.2 Deep Learning Models in Healthcare Simulation
9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model
9.5 Conclusion
References
10. Wolfram’s Cellular Automata Model in Health Informatics
Sutapa Sarkar and Mousumi Saha
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
Part III: Machine Learning and COVID Prospective
11. COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques
Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena
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
11.4.3 Back Propagation Neural Network
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
Sivanantham Kalimuthu
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
Sayan Das and Jaya Sil
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
13.4 Results and Discussion
13.5 Conclusion
References
14. Face Mask Detection in Real-Time Video Stream Using Deep Learning
Alok Negi and Krishan Kumar
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
Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar
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
Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari
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 Microcontrolle
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
16.3.1.3 Protocols Used
16.3.1.4 Libraries Used
16.4 Simulation Results
16.5 Conclusion
16.6 Abbreviations and Acronyms
References
17. COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques
Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik
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
Abhilash C. B. and Kavi Mahesh
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 Congeegation 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
Part IV: Prospective of Computational Intelligence in Healthcare
19. Conceptualizing Tomorrow’s Healthcare Through Digitization
Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena
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 363 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
Pitambar Behera and Om Prakash Jena
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 Target
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
Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas
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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