Next Generation Healthcare Systems Using Soft Computing Techniques (Artificial Intelligence in Smart Healthcare Systems)

✍ Scribed by Rekh Ram Janghel (editor), Rohit Raja (editor), Korhan Cengiz (editor), Hiral Raja (editor)

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 225
• Edition: 1
• Category: Library

✦ Synopsis

This book presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. Written as a guide for assessing the roles that these techniques play, the book also highlights implementation strategies, lists problem-solving solutions, and paves the way for future research endeavors in smart and next-generation healthcare systems.

This book provides applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statical mathematics, play in the advancements of smart healthcare systems. The book presents the basics as well as the advanced concepts to help beginners, as well as industry professionals, get up to speed on the latest developments in healthcare systems. The book examines descriptive, predictive, and social network techniques and discusses analytical tools and the important role they play in finding solutions to problems in healthcare systems. A framework of robust and novel healthcare techniques is highlighted, as well as implementation strategies and a setup for future research endeavors.

Healthcare Systems Using Soft Computing Techniques is a valuable resource for researchers and postgraduate students in healthcare systems engineering, computer science, information technology, and applied mathematics. The book introduces beginners to―and at the same time brings industry professionals up to speed with―the important role soft computing techniques play in smart healthcare systems.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
1. Computational Intelligence for Healthcare
1.1 Introduction
1.1.1 Artificial Neural Network
1.1.2 Restricted Boltzman Machines
1.1.3 Support Vector Machines
1.1.4 Evolutionary Algorithms
1.1.5 Fuzzy Systems
1.1.6 Swarm Intelligence
1.2 Issues and Challenges
1.2.1 Data Inconsistency, Inaccuracy, and Missing Values
1.2.2 Imbalanced Data
1.2.3 Data Collection Cost
1.2.4 Huge Data Volume
1.2.5 Ethical and Privacy Issues
1.3 Feature Engineering
1.3.1 Feature Extraction
1.3.2 Feature Selection
1.3.2.1 Filter Method
1.3.2.2 Wrapper Method
1.3.2.3 Embedded Method
1.3.3 Feature Weighting
1.3.4 Introduction to Gene Expression Dataset
1.3.5 Challenges in Gene Expression Data
1.3.6 Feature Selection and Classification of Gene Expression Data Using Binary Jaya Algorithm
1.3.6.1 Binary Jaya Algorithm
1.3.6.2 Use of Feature Selection with Binary Jaya Algorithm
1.3.6.3 Result and Discussion
1.4 Available Resources
1.5 Conclusion
References
2. Analysis of Recurrent Neural Network and Convolution Neural Network Techniques in Blood Cell Classification
2.1 Introduction
2.1.1 Deep Learning Techniques
2.1.2 Medical Imaging/White Blood Cell Classification
2.2 Dataset
2.3 Analysis of Deep Learning Techniques
2.3.1 Recurrent Neural Network
2.3.2 Convolution Neural Network
2.3.3 Convolution Neural Network Experiment Design and Results
2.3.3.1 Case # 1
2.3.3.2 Case # 2
2.3.3.3 Case # 3
2.4 Conclusions
References
3. Evaluating the Effectiveness of the Convolution Neural Network in Detecting Brain Tumors
3.1 Introduction
3.1.1 Deep Learning and Convolution Neural Network
3.1.2 Medical Imagery/Brain Tumor Detection
3.2 Related Work
3.3 Dataset
3.4 Evaluation of Convolution Neural Network Architectures
3.4.1 Test Case # 1
3.4.2 Test Case # 2
3.4.3 Test Case # 3
3.5 Conclusions
References
4. Implementation of Machine Learning in Color Perception and Psychology: A Review
4.1 Introduction
4.1.1 Motivation
4.1.2 Related Works
4.1.3 Contribution
4.2 Application Areas
4.2.1 Food and Breed Hunting for Animals
4.2.2 Application in Color Constancy
4.2.3 Color Blindness Detection
4.2.4 Sentiment Analysis Based on Color Attributes
4.2.5 Application in Agriculture Using Color Classification
4.3 Deep Learning Methods Used in Color Psychology Analysis
4.3.1 Conditional GAN
4.3.2 Convolution Neural Network
4.3.3 Bidirectional Long Short Term Memory
4.3.4 Probabilistic Neural Network
4.3.5 VGG-16
4.3.6 DenseNet
4.4 Conclusion
References
5. Early Recognition of Dynamic Sleeping Patterns Associated with Rapid Eyeball Movement Sleep Behavior Disorder of Apnea Patients Using Neural Network Techniques
5.1 Introduction
5.2 Methodologies
5.3 Data Collection
5.4 Preprocessing of Data and Feature Extraction Using Wavelet Packet Decomposition (WPD)
5.5 Feature Classification by Deep Neural Network (DNN) Classifier
5.6 Long Short Term Memory (LSTM) Technique
5.7 Results and Discussions
5.8 Conclusion
References
6. Smart Attendance cum Health Check-up Machine for Students/Villagers/Company Employees
6.1 Introduction
6.2 Overview of Various Possible Smart Attendance Systems
6.2.1 Proposed Solution
6.2.2 Face Recognition System
6.2.2.1 Face Detection
6.2.2.2 Face Recognition
6.3 Health Parameters and Stress Detection
6.3.1 Eye Blink Detection
6.3.2 Emotion Detection
6.4 Physiological Parameters and Sensors Description
6.4.1 Body Temperature
6.4.2 Blood SpO2 Level
6.4.3 Heart Rate
6.5 Hardware and Sensors Used
6.5.1 MLX 90614 Temperature Sensor
6.5.2 MAX 30100 Heart Rate and Blood SpO2 Sensor
6.5.3 Arduino UNO
6.6 Software Tools
6.6.1 Arduino IDE
6.6.2 Tkinter
6.7 Outcomes and Result
6.7.1 Face Recognition
6.7.2 Emotion Recognition and Eye-Blink Rate Detection
6.7.3 Data from Sensors
6.8 Conclusion
Acknowledgement
References
7. Oral Histopathological Photomicrograph Classification Using Deep Learning
7.1 Introduction
7.2 Related Work
7.3 Present Diagnosing Method for Oral Cancer
7.4 Materials and Methods
7.4.1 Deep-Learning Combined with SVM Approach
7.4.2 Transfer Learning of the Deep-Learning Model's Approach
7.4.3 Fusion of the Results Obtained from Transfer Learning and SVM Process
7.5 Results and Discussions
7.5.1 Comparison of the Results with Other Related Works
7.6 Conclusion
References
8. Prediction of Stage of Alzheimer's Disease DenseNet Deep Learning Model
8.1 Introduction
8.2 Literature Survey
8.3 Methodology
8.3.1 Deep Learning Techniques
8.3.2 Dataset
8.3.3 Data Preprocessing
8.3.4 Network Architecture
8.3.5 DenseNet
8.4 Experiment and Result Discussion
8.4.1 Using Machine Learning
8.4.2 Using Deep Learning
8.5 Conclusion
References
9. An Insight of Deep Learning Applications in the Healthcare Industry
9.1 Introduction
9.2 Drug Discovery
9.3 Medical Image and Diagnostics
9.4 Clinical Trials
9.5 Patient Monitoring and Personalized Treatment
9.6 Chatbot Using NLP
9.7 Health Insurance and Fraud Detection
9.8 Medical Diagnosis
9.9 Future Development
References
10. Expand Patient Care with AWS Cloud for Remote Medical Monitoring
10.1 Introduction
10.2 Literature Review
10.3 Cloud Healthcare Management
10.3.1 Cloud Computing Models
10.3.1.1 Infrastructure as a Service (IaaS)
10.3.1.2 Platform as a Service (PaaS)
10.3.1.3 Software as a Service (SaaS)
10.3.2 Deployment Models
10.3.2.1 Cloud
10.3.2.2 Hybrid
10.3.2.3 On-Premises
10.3.3 Advantages of Cloud Computation
10.4 Amazon Web Services
10.4.1 Compute
10.4.1.1 Amazon Elastic Compute Cloud
10.4.1.2 AWS Elastic Beanstalk
10.4.2 Storage
10.4.2.1 Amazon Elastic Block Store (EBS Volumes)
10.4.2.2 Amazon Elastic File System (Amazon EFS)
10.4.3 Amazon Machine Learning
10.4.4 Big Data Analysis in AWS
10.5 Cloud Pricing Strategy
10.6 AWS - Healthcare Solutions
10.7 Conclusion
References
11. Privacy and Security Solution in Wireless Sensor Network for IoT in Healthcare System
11.1 Introduction
11.2 Classification of WSNs Protocols
11.2.1 Data-Centric Routing Protocol
11.2.2 Multiple Path Routing Protocol
11.2.2.1 Disjoint Path Routing Protocol
11.2.2.2 Braided Path Routing Protocol
11.2.2.3 N to 1 Multipath Discovery Routing Protocol
11.2.3 Hierarchical Routing Protocol
11.2.3.1 Initial Phase
11.2.3.2 Neighbor Discovery Phase
11.2.3.3 Clustering Phase
11.2.3.4 Data Message Exchange Phase
11.2.4 Routing Protocol Based on Location
11.2.5 Mobility-Based Routing Protocol
11.2.6 Quality of Service-Based Routing Protocol
11.2.6.1 Sequential Assignment Outing (SAR) Protocol
11.2.6.2 SPEED Protocol
11.2.6.3 Quality of Service - Aware and Heterogeneously Clustered Routing Protocol (QHCR)
11.3 Privacy and Security Issues in WSN
11.3.1 Security and Privacy Issues
11.3.1.1 Denial of Service Attack
11.3.1.2 Manipulating Routing Information
11.3.1.3 Sybil Attack
11.3.1.4 Sinkhole Attack
11.3.2 Clone Attack
11.3.3 Selective Forwarding Attack
11.3.3.1 HELLO Flood Attack
11.4 Security and Privacy Solutions
11.4.1 Use of Effective Key Management
11.4.2 Use of Efficient Public Key Infrastructure
11.4.3 Effective Use of Multiclass Nodes
11.4.4 Efficient Clustering of Modules to Increase Safety of WSN
11.4.5 Point-to-Point Protection Approach
11.4.6 Registration and Key Management Phase
11.4.7 Secure Data Exchange Phase
11.4.8 Generating Perturb Phase
11.4.9 Signature and Perturbation Phase
11.4.10 Authentication Phase
11.4.11 Decryption and Authentication
11.5 Conclusion
References
12. An Epileptic Seizure Detection and Classification Based on Machine Learning Techniques
12.1 Introduction
12.2 Related Work
12.3 Proposed Methodology
12.3.1 Database Description - BONN University EEG Dataset
12.3.1.1 Data Pre-processing
12.3.1.2 Statistical Features of the Dataset
12.3.1.2.1 Common EEG Artifacts
12.3.1.2.2 Features Extraction
12.3.2 Evaluation Assessment Method
12.3.3 Classification Techniques
12.3.3.1 Support Vector Machines (SVMs)
12.3.3.2 Random Forests
12.3.3.3 Extreme Learning Machine
12.3.3.4 K-Nearest Neighbors
12.3.3.5 Logistic Regression
12.3.3.6 Decision Trees
12.3.3.7 Multilayer Perceptron
12.3.3.8 Ensemble Classifiers
12.4 Experimental Results
12.5 Discussion
12.6 Conclusion
References
13. Analysis of Coronary Artery Disease Using Various Machine Learning Techniques
13.1 Introduction
13.2 Literature Survey
13.3 Material and Methods
13.3.1 Dataset Description
13.4 Methodology
13.4.1 Data Normalization
13.4.1.1 Data Splitting
13.4.1.2 Classification Models
13.4.1.3 Support Vector Machine
13.4.1.4 Decision Tree
13.4.1.5 Random Forest
13.4.1.6 K‑Nearest Neighbor (K‑NN)
13.4.2 Logistic Regression
13.4.3 Types of Logistic Regression
13.4.3.1 Logistic Regression Assumptions
13.4.3.2 Naïve Bayes
13.4.3.3 XG-Boost
13.5 Result Analysis
13.5.1 Performance Comparison of Algorithms
13.6 Discussion
13.7 Conclusion
References
Index