Artificial Intelligence for the Internet of Health Things 1st Edition by Shankar, Eswaran Perumaln, Deepak Gupta 0367746905 9780367746902

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ISBN 10: 0367746905

ISBN 13: 9780367746902

Author: K. Shankar; Eswaran Perumal; Deepak Gupta

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

Artificial Intelligence for the Internet of Health Things 1st Table of contents:

Chapter 1 Artificial Intelligence (AI) for IoHT – An Introduction

1.1 Artificial Intelligence (AI) in the Healthcare Domain

1.2 Evolution of AI in the Medical Sector

1.3 Use of AI Devices for Clinical Data Generation

1.4 Types of AI of Relevance to Healthcare

1.4.1 Machine Learning – Neural Networks and Deep Learning

1.4.2 Natural Language Processing

1.4.3 Rule-Based Expert Systems

1.4.4 Physical Robots

1.4.5 Robotic Process Automation

1.5 AI-Based Applications in Healthcare

1.5.1 Patient Engagement and Adherence Applications

1.5.2 Administrative Applications

1.5.3 Implications for the Healthcare Workforce

1.5.4 Ethical Implications

1.6 Conclusion

References

Chapter 2 Role of Internet of Things and Cloud Computing Technologies in the Healthcare Sector

2.1 Introduction

2.2 IoT-Based Healthcare Framework

2.3 Cloud Computing for Healthcare

2.4 IoT-Based Healthcare Services and Applications

2.4.1 IoT Healthcare Services

2.4.1.1 The Internet of m-Health Things

2.4.1.2 Adverse Drug Reactions

2.4.1.3 Community Healthcare

2.4.1.4 Children’s Health Information

2.4.2 IoT Healthcare Applications

2.4.2.1 Glucose Level Sensing

2.4.2.2 Electrocardiogram Monitoring

2.4.2.3 Blood Pressure Monitoring

2.4.2.4 Body Temperature Monitoring

2.4.3 IoT Healthcare: Current Issues and Challenges

2.4.3.1 Cost Analysis

2.4.3.2 Continuous Monitoring

2.4.3.3 Identification

2.4.3.4 Mobility

2.5 Components in IoT-Based Healthcare Services

2.5.1 IoT Devices

2.5.2 Wireless Technologies for IoT

2.5.3 Web Technologies for IoT

2.6 Conclusion

References

Chapter 3 An Extensive Overview of Wearable Technologies in the Healthcare Sector

3.1 Introduction

3.2 Background Information

3.2.1 Quantified Self

3.2.2 Wearable Technology

3.2.3 Advantages of Wearable Technology

3.3 Challenges

3.3.1 Sustainability

3.3.2 Digital Divide

3.3.3 Failure Rates

3.3.4 Lack of Predictive Comparability

3.3.5 Privacy and Security

3.4 Typical Wearable Devices with Applications in Health

3.4.1 Wearable Devices Used for General Health Management

3.4.2 Wearable Biosensors Revolutionizing In-Clinic/Hospital Care

3.4.3 Wearable Biosensors Revolutionizing Specific Fields of Healthcare Outside of the Hospital and Clinic

3.4.4 Regulatory Oversight and Economic Impact

3.5 Conclusion

References

Chapter 4 IoHT and Cloud-Based Disease Diagnosis Model Using Particle Swarm Optimization with Artificial Neural Networks

4.1 Introduction

4.2 The Proposed Model

4.2.1 Data Collection

4.2.2 PSO-ANN Model

4.2.2.1 ANN Model

4.2.2.2 Parameter Optimization of ANN Using PSO

4.2.3 Proposed Diagnostic Model

4.3 Performance Validation

4.4 Conclusion

References

Chapter 5 IoHT-Based Improved Grey Optimization with Support Vector Machine for Gastrointestinal Hemorrhage Detection and Diagnosis Model

5.1 Introduction

5.2 Proposed Method

5.2.1 Preprocessing

5.2.2 Feature Extraction Process Using NGLCM

5.2.3 NGLCM-IGWO-SVM–Based Classification

5.3 Experimental Validation

5.3.1 Data Set Used

5.3.2 Results Analysis

5.4 Conclusion

References

Chapter 6 An Effective-Based Personalized Medicine Recommendation System Using an Ensemble of Extreme Learning Machine Model

6.1 Introduction

6.2 The Proposed Medical Recommender System

6.2.1 Database System Module

6.2.2 Data Preparation Module

6.2.3 Recommendation Model Module

6.2.4 Model Evaluation Module

6.2.5 Proposed Recommendation Model

6.2.5.1 Extreme Learning Machine (ELM)

6.2.5.2 b-ELM Classifier

6.3 Experimental Results and Discussion

6.4 Conclusion

References

Chapter 7 A Novel MapReduce-Based Hybrid Decision Tree with TFIDF Algorithm for Public Sentiment Mining of Diabetes Mellitus

7.1 Introduction

7.2 The Proposed Model

7.2.1 Data Collection

7.2.2 Data Preprocessing and Integration

7.2.2.1 Data Tokenization

7.2.2.2 Generating and Removing Stop Words

7.2.2.3 Detecting Stop Words with SentiCircles

7.2.2.4 Stemming and Lemmatization

7.2.2.5 Corpus Generation

7.2.2.6 Tagging

7.2.3 Data Analysis Stage

7.2.3.1 MPHDT-T-Based Opinion Mining

7.2.3.2 Decision Tree (DT)

7.2.3.3 Term Frequency-Inverse Document Frequency (TFIDF)

7.3 Performance Validation

7.4 Conclusion

References

Chapter 8 IoHT with Artificial Intelligence–Based Breast Cancer Diagnosis Model

8.1 Introduction

8.2 Related Works

8.3 The Proposed Model

8.3.1 Image Acquisition

8.3.2 Preprocessing

8.3.3 HFE Model

8.3.3.1 Homogeneity

8.3.3.2 Energy

8.3.3.3 HOG Features

8.3.4 GA-SVM-Based Classification

8.3.4.1 Chromosome Design

8.3.4.2 Fitness Function

8.3.4.3 Hybridization of the GA-SVM Algorithm

8.4 Performance Validation

8.5 Conclusion

References

Chapter 9 Artificial Intelligence with a Cloud-Based Medical Image Retrieval System Using a Deep Neural Network

9.1 Introduction

9.2 The Proposed DC-DNN Model

9.2.1 DLTerQEP-Based Texture Feature Extraction

9.2.2 Crest Line–Based Shape Feature Extraction

9.2.2.1 Curvature Approximation

9.2.2.2 Crest Point Classification

9.2.2.3 Crest Lines

9.2.3 Euclidean Distance–Based Similarity Measurement

9.2.4 DNN-Based Classification

9.3 Performance Validation

9.3.1 Data Set Used

9.3.2 Results Analysis

9.4 Conclusion

References

Chapter 10 IoHT with Cloud-Based Brain Tumor Detection Using Particle Swarm Optimization with Support Vector Machine

10.1 Introduction

10.2 The Proposed GLCM-PSO-SVM Model

10.2.1 Preprocessing

10.2.2 Feature Extraction

10.2.3 PSO-SVM-Based Classification

10.2.3.1 SVM Classifier

10.2.3.2 Parameter Optimization of SVM Using the PSO Algorithm

10.3 Experimental Analysis

10.3.1 Data Set Description

10.3.2 Evaluation Metrics

10.3.3 Results Analysis

10.4 Conclusion

References

Chapter 11 Artificial Intelligence-Based Hough Transform with an Adaptive Neuro-Fuzzy Inference System for a Diabetic Retinopathy Classification Model

11.1 Introduction

11.2 Proposed Method

11.2.1 Preprocessing

11.2.2 Watershed-Based Segmentation

11.2.3 Hough Transform–Based Feature Extraction

11.2.4 ANFIS-Based Classification

11.3 Performance Analysis

11.3.1 Data Set Description

11.3.2 Results Analysis

11.4 Conclusion

References

Chapter 12 An IoHT–Based Intelligent Skin Lesion Detection and Classification Model in Dermoscopic Images

12.1 Introduction

12.2 The SIFT-SVM

12.2.1 Bilateral Filtering–Based Preprocessing

12.2.2 Image Segmentation

12.2.3 Feature Extraction

12.2.3.1 Detect Scale-Space Extrema

12.2.3.2 Localized Feature Points

12.2.3.3 Assignment of Orientation

12.2.3.4 Feature Point Descriptor

12.2.4 Image Classification

12.3 Performance Validation

12.3.1 Data Set Used

12.3.2 Results Analysis

12.4 Conclusion

References

Chapter 13 An IoHT-Based Image Compression Model Using Modified Cuckoo Search Algorithm with Vector Quantization

13.1 Introduction

13.2 Vector Quantization and LBG Algorithm

13.3 Proposed MCS-LBG Algorithm–Based VQ

13.3.1 CS Algorithm

13.3.2 MCS Algorithm

13.3.3 Working Process of MCS-LBG Algorithm

13.4 Performance Validation

13.5 Conclusion

References

Chapter 14 An Effective Secure Medical Image Transmission Using Improved Particle Swarm Optimization and Wavelet Transform

14.1 Introduction

14.2 The IPSO-DWT Method

14.2.1 Embedded Procedure

14.2.1.1 Representation of the Stego Key

14.2.1.2 Embedded Process

14.2.2 Extraction of the Secret Message

14.2.3 IPSO-Based Pixel Selection Process

14.3 Performance Validation

14.4 Conclusion

References

Chapter 15 IoHT with Wearable Devices–Based Feature Extraction and a Deep Neural Networks Classification Model for Heart Disease Diagnosis

15.1 Introduction

15.2 Proposed Model

15.2.1 IoHT-Based Patient Data Collection

15.2.2 IoHT Medical Data Preprocessing

15.2.3 Heart Feature Extraction

15.2.4 DNN for Data Classification

15.2.4.1 Training of Layers

15.2.4.2 The Stacked Autoencoder

15.2.4.3 The Softmax Layer

15.3 Performance Validation

15.4 Conclusion

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