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Tele-Healthcare

Applications of Artificial Intelligence and Soft Computing Techniques

Edited by R Nidhya, Manish Kumar and S. Balamurugan
Series: Artificial Intelligence and Soft Computing for Industrial Transformation
Copyright: 2022   |   Status: Published
ISBN: 9781119841760  |  Hardcover  |  
389 pages
Price: $225 USD
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One Line Description
This book elucidates all aspects of tele-healthcare which is the application of AI, soft computing, digital information, and communication technologies, to provide services remotely and manage one’s healthcare.

Audience
Researchers and post-graduate students in biomedical engineering, artificial intelligence, and information technology; medical doctors and practitioners and industry experts in the healthcare sector; healthcare sector network administrators.

Description
Throughout the world, there are huge developing crises with respect to healthcare workforce shortages, as well as a growing burden of chronic diseases. As a result, e-health has become one of the fastest-growing service areas in the medical sector. E-health supports and ensures the availability of proper healthcare, public health, and health education services at a distance and in remote places. For the sector to grow and meet the need of the marketplace, e-health applications have become one of the fastest growing areas of research. However, to grow at a larger scale requires the following:
1. The availability of user cases for the exact identification of problems that need to be visualized.
2. A well-supported market that can promote and adopt the e-health care concept.
3. Development of cost-effectiveness applications and technologies for successful
implementation of e-health at a larger scale.
This book mainly focuses on these three points for the development and implementation of e-health services globally.
In this book the reader will find:
• Details of the challenges in promoting and implementing the telehealth industry.
• How to expand a globalized agenda of personalized telehealth in integrative medical
treatment for disease diagnosis and its industrial transformation.
• How to design machine learning techniques for improving the tele-healthcare system.

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Author / Editor Details
R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated to Jawaharlal Nehru Technical University, Anantapuram, India. She has published many research articles in SCI journals and her research interests include wireless body area networks, network security, and data mining.

Manish Kumar, PhD, is an assistant professor in the School of Computer Science & Engineering, VIT Chennai. His research interests include soft computing applications for bioinformatics problems and computational intelligence.

S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.

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Table of Contents
Preface
1. Machine Learning–Assisted Remote Patient Monitoring with Data Analytics Vinutha D. C., Kavyashree and G. T. Raju
1.1 Introduction
1.1.1 Traditional Patient Monitoring System
1.1.2 Remote Monitoring System
1.1.3 Challenges in RPM
1.2 Literature Survey
1.2.1 Machine Learning Approaches in Patient Monitoring
1.3 Machine Learning in RPM
1.3.1 Support Vector Machine
1.3.2 Decision Tree
1.3.3 Random Forest
1.3.4 Logistic Regression
1.3.5 Genetic Algorithm
1.3.6 Simple Linear Regression
1.3.7 KNN Algorithm
1.3.8 Naive Bayes Algorithm
1.4 System Architecture
1.4.1 Data Collection
1.4.2 Data Pre-Processing
1.4.3 Apply Machine Learning Algorithm and Prediction
1.5 Results
1.6 Future Enhancement
1.7 Conclusion
References
2. A Survey on Recent Computer-Aided Diagnosis for Detecting Diabetic Retinopathy
Priyadharsini C., Jagadeesh Kannan R. and Farookh Khadeer Hussain
2.1 Introduction
2.2 Diabetic Retinopathy
2.2.1 Features of DR
2.2.2 Stages of DR
2.3 Overview of DL Models
2.3.1 Convolution Neural Network
2.3.2 Autoencoders
2.3.3 Boltzmann Machine and Deep Belief Network
2.4 Data Set
2.5 Performance Metrics
2.6 Literature Survey
2.6.1 Segmentation of Blood Vessels
2.6.2 Optic Disc Feature
2.6.3 Lesion Detections
2.6.3.1 Exudate Detection
2.6.3.2 MA and HM
2.6.4 DR Classification
2.7 Discussion and Future Directions
2.8 Conclusion
References
3. A New Improved Cryptography Method-Based e-Health Application in Cloud Computing Environment
Dipesh Kumar, Nirupama Mandal and Yugal Kumar
3.1 Introduction
3.1.1 Contribution
3.2 Motivation
3.3 Related Works
3.4 Challenges
3.5 Proposed Work
3.6 Proposed Algorithm for Encryption
3.6.1 Demonstration of Encryption Algorithm
3.6.1.1 When the Number of Columns Selected in the Table is Even
3.6.1.2 When the Number of Columns Selected in the Table is Odd
3.6.2 Flowchart for Encryption
3.7 Algorithm for Decryption
3.7.1 Demonstration of Decryption Algorithm
3.7.1.1 When the Number of Columns Selected in the Table is Even
3.7.1.2 When the Number of Columns Selected in the Table is Odd
3.7.2 Flowchart of Decryption Algorithm
3.8 Experiment and Result
3.9 Conclusion
References
4. Cutaneous Disease Optimization Using Teledermatology Underresourced Clinics
Supriya M., Murugan K., Shanmugaraja T. and Venkatesh T.
4.1 Introduction
4.2 Materials and Methods
4.2.1 Clinical Setting and Teledermatology Workflow
4.2.2 Study Design, Data Collection, and Analysis
4.3 Proposed System
4.3.1 Teledermatology in an Underresourced Clinic
4.3.2 Teledermatology Consultations from Uninsured Patients
4.3.3 Teledermatology for Patients Lacking Access to Dermatologists
4.3.4 Teledermatologist Management from Nonspecialists
4.3.5 Segment Factors of Referring PCPs and Their Patients
4.3.6 Teledermatology Operational Considerations
4.3.7 Instruction of PCPs
4.4 Challenges
4.5 Results and Discussion
4.5.1 Challenges of Referring to Teledermatology Services
References
5. Cognitive Assessment Based on Eye Tracking Using Device-Embedded Cameras via Tele-Neuropsychology
Shanmugaraja T., Venkatesh T., Supriya M. and Murugan K.
5.1 Introduction
5.2 Materials and Methods
5.3 Framework Elements
5.3.1 Eye Tracker Camera
5.3.2 Test Construction
5.3.3 Web Camera
5.3.4 Camera for Eye Tracking
5.4 Proposed System
5.4.1 Camera for Tracking Eye
5.4.2 Web Camera
5.4.3 Scoring
5.4.4 Eye Tracking Camera
5.4.5 Web Camera Human-Coded Scoring
5.5 Subjects
5.5.1 Characteristics of Subject
5.6 Methodology
5.6.1 Analysis of Data
5.7 Results
5.8 Discussion
5.9 Conclusion
References
6. Fuzzy-Based Patient Health Monitoring System
Venkatesh T., Murugan K., Supriya M., Shanmugaraja T. and Rekha Chakravarthi
6.1 Introduction
6.1.1 General Problem
6.1.2 Existing Patient Monitoring and Diagnosis Systems
6.1.3 Fuzzy Logic Systems
6.2 System Design
6.2.1 Hardware Requirements
6.2.1.1 Functional Requirements
6.2.1.2 Nonfunctional Specifications
6.3 Software Architecture
6.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API)
6.3.2 Flowchart—API
6.3.3 Foreign Tag IDs
6.3.4 Database Manager
6.3.5 Database Designing
6.3.6 The Fuzzy Logic System
6.3.6.1 Introduction to Fuzzy Logic
6.3.6.2 The Modified Prior Alerting Score (MPAS)
6.3.6.3 Structure of the Fuzzy Logic System
6.3.7 Designing a System in Fuzzy
6.3.7.1 Input Variables
6.3.7.2 The Output Variable
6.4 Results and Discussion
6.4.1 Hardware Sensors Validation
6.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine
6.4.3 Normal Group (NRM)
6.4.4 Low Risk Group
6.4.5 High Risk Group (HRG)
6.5 Conclusions and Future Work
6.5.1 Summary and Concluding Remarks
6.5.2 Future Directions
References
7. Artificial Intelligence: A Key for Detecting COVID-19 Using Chest Radiography
C. Vinothini, P. Anitha, Priya J., Abirami A. and Akash S.
7.1 Introduction
7.2 Related Work
7.2.1 Traditional Approach
7.2.2 Deep Learning–Based Approach
7.3 Materials and Methods
7.3.1 Data Set and Data Pre-Processing
7.3.2 Proposed Model
7.4 Experiment and Result
7.4.1 Experiment Setup
7.4.2 Comparison with Other Models
7.5 Results
7.6 Conclusion
References
8. An Efficient IoT Framework for Patient Monitoring and Predicting Heart Disease Based on Machine Learning Algorithms
Shanthi S., Nidhya R., Uma Perumal and Manish Kumar
8.1 Introduction
8.2 Literature Survey
8.3 Machine Learning Algorithms
8.4 Problem Statement
8.5 Proposed Work
8.5.1 Data Set Description
8.5.2 Collection of Values Through Sensor Nodes
8.5.3 Storage of Data in Cloud
8.5.4 Prediction with Machine Learning Algorithms
8.5.4.1 Data Cleaning and Preparation
8.5.4.2 Data Splitting
8.5.4.3 Training and Testing
8.5.5 Machine Learning Algorithms
8.5.5.1 Naive Bayes Algorithm
8.5.5.2 Decision Tree Algorithm
8.5.5.3 K-Neighbors Classifier
8.5.5.4 Logistic Regression
8.6 Performance Analysis and Evaluation
8.7 Conclusion
References
9. BABW: Biometric-Based Authentication Using DWT and FFNN
R. Kingsy Grace, M.S. Geetha Devasena and R. Manimegalai
9.1 Introduction
9.2 Literature Survey
9.3 BABW: Biometric Authentication Using Brain Waves
9.4 Results and Discussion
9.5 Conclusion
References
10. Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review
Pavithra D., Jayanthi A. N., Nidhya R. and Balamurugan S.
10.1 Introduction
10.2 Autism Screening Methods
10.2.1 Autism Screening Instrument for Educational Planning—3rd Version
10.2.2 Quantitative Checklist for Autism in Toddlers
10.2.3 Autism Behavior Checklist
10.2.4 Developmental Behavior Checklist-Early Screen
10.2.5 Childhood Autism Rating Scale Version 2
10.2.6 Autism Spectrum Screening Questionnaire (ASSQ)
10.2.7 Early Screening for Autistic Traits
10.2.8 Autism Spectrum Quotient
10.2.9 Social Communication Questionnaire
10.2.10 Child Behavior Check List
10.2.11 Indian Scale for Assessment of Autism
10.3 Machine Learning in ASD Screening and Diagnosis
10.4 DL in ASD Diagnosis
10.5 Conclusion
References
11. Drug Target Module Mining Using Biological Multifunctional Score-Based Coclustering
R. Gowri and R. Rathipriya
11.1 Introduction
11.2 Literature Study
11.3 Materials and Methods
11.3.1 Biological Terminologies
11.3.2 Functional Coherence
11.3.3 Biological Significances
11.3.4 Existing Approach: MR-CoC
11.4 Proposed Approach: MR-CoCmulti
11.4.1 Biological Score Measures for DTM
11.4.2 Multifunctional Score-Based Co-Clustering Approach
11.5 Experimental Analysis
11.5.1 Experimental Results
11.6 Discussion
11.7 Conclusion
Acknowledgment
References
12. The Ascendant Role of Machine Learning Algorithms in the Prediction of Breast Cancer and Treatment Using Telehealth
Jothi K.R., Oswalt Manoj S., Ananya Singhal and Suruchi Parashar
12.1 Introduction
12.1.1 Objective
12.1.2 Description and Goals
12.1.2.1 Data Exploration
12.1.2.2 Data Pre-Processing
12.1.2.3 Feature Scaling
12.1.2.4 Model Selection and Evaluation
12.2 Literature Review
12.3 Architecture Design and Implementation
12.4 Results and Discussion
12.5 Conclusion
12.6 Future Work
References
13. Remote Patient Monitoring: Data Sharing and Prediction Using Machine Learning
Mohammed Hameed Alhameed, S. Shanthi, Uma Perumal and Fathe Jeribi
13.1 Introduction
13.1.1 Patient Monitoring in Healthcare System
13.2 Literature Survey
13.3 Problem Statement
13.4 Machine Learning
13.4.1 Introduction
13.4.2 Cloud Computing
13.4.3 Design and Architecture
13.5 Proposed System
13.6 Results and Discussions
13.7 Privacy and Security Challenges
13.8 Conclusions and Future Enhancement
References
14. Investigations on Machine Learning Models to Envisage Coronavirus in Patients
R. Sabitha, J. Shanthini, R.M. Bhavadharini and S. Karthik
14.1 Introduction
14.2 Categories of ML Algorithms in Healthcare
14.3 Why ML to Fight COVID-19? Tools and Techniques
14.4 Highlights of ML Algorithms Under Consideration
14.5 Experimentation and Investigation
14.6 Comparative Analysis of the Algorithms
14.7 Scope of Enhancement for Better Investigation
References
15. Healthcare Informatics: Emerging Trends, Challenges, and Analysis of Medical Imaging
G. Karthick and N.S. Nithya
15.1 Emerging Trends and Challenges in Healthcare Informatics
15.1.1 Advanced Technologies in Healthcare Informatics
15.1.2 Intelligent Smart Healthcare Devices Using IoT With DL
15.1.3 Cyber Security in Healthcare Informatics
15.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics
15.2 Performance Analysis of Medical Image Compression Using Wavelet Functions
15.2.1 Introduction
15.2.2 Materials and Methods
15.2.3 Wavelet Basis Functions
15.2.3.1 Haar Wavelet
15.2.3.2 db Wavelet
15.2.3.3 bior Wavelet
15.2.3.4 rbio Wavelet
15.2.3.5 Symlets Wavelet
15.2.3.6 coif Wavelet
15.2.3.7 dmey Wavelet
15.2.3.8 fk Wavelet
15.2.4 Compression Methods
15.2.4.1 Embedded Zero-Trees of Wavelet Transform
15.2.4.2 Set Partitioning in Hierarchical Trees
15.2.4.3 Adaptively Scanned Wavelet Difference Reduction
15.2.4.4 Coefficient Thresholding
15.3 Results and Discussion
15.3.1 Mean Square Error
15.3.2 Peak Signal to Noise Ratio
15.4 Conclusion
15.4.1 Summary
References
Index

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