Explainable Artificial Intelligence in Healthcare Systems

Contents Preface Acknowledgements Section 1. Primitive Concepts under Explainable AI Chapter 1 XAI in Healthcare: Black Box to Interpretable Models Abstract 1.1 Introduction 1.2 Significance of XAI 1.3 Motivation for Applying XAI in Health Care 1.4 Advantages in Healthcare 1.5 XAI Methods 1.6 Commonly Used XAI Methods 1.7 Interprétable Machine Learning Model 1.8 Comparative Study of Four Models 1.9 Applications of XAI 1.10 Limitations of XAI and Future Developments and Trends 1.11 Conclusion References Chapter 2 Healthcare XAI: A Systematic Study Abstract 2.1 Introduction 2.2 Framework from Machine Learning to XAI Model-Specific vs Model-Agnostic Global Methods vs Local Methods Before-Modelling vs During-Modelling vs After-Modelling Surrogate Techniques vs Visualization Techniques 2.3 Knowledge Representation of XAI 2.4 Interpretability and Explainability Interpretability Explainability Importance of Interpretability and Explainability Interpretability vs Explainability 2.5 XAI Approaches and Analysis in Sustainable Smart Healthcare Informatics 2.6 Role of AI 2.7 XAI Approaches 2.8 On Federated Learning and the Role of Federated Learning with XAI in Health Sector 2.9 Threats of XAI in Healthcare 2.10 Opportunities of XAI 2.11 Results and Analysis 2.12. Conclusion References Chapter 3 XAI in Healthcare: A SWOT Analysis Abstract 3.1 Introduction 3.2 Healthcare 5.0 3.3 XAI 3.3.1. Definitions 3.4 XAI Process 3.5 Pillars of XAI 3.6 XAI – SWOT Analysis 3.6.1 Strength 3.6.2 Weakness 3.6.3 Opportunities 3.6.4 Threats 3.7. Conclusion References Chapter 4 A Comprehensive Review on the Developments in Explainable Artificial Intelligence Abstract 4.1 Introduction 4.2 Explainable Artificial Intelligence Literature Review 4.3 Explainable AI Systems 4.4 Applications of XAI 4.4.1 XAI in Various Domains 4.4.2 XAI in Industrial Applications 4.5 Advancements in XAI 4.5.1 Logic-Based Strategy 4.5.2 Taxonomies 4.5.3 Real-Time Explanations 4.5.4 Using Graph Neural Networks in XAI 4.5.5 Wikipedia Knowledge Graph 4.5.6 Frameworks 4.5.7 Changes Suggested in XAI 4.6 Advantages and Disadvantages of XAI Systems 4.7 Conclusion and Future Scope References Chapter 5 Unveiling the Algorithms: How Explainable AI Reshapes Healthcare Abstract 5.1 Introduction 5.2 Role of AI in Healthcare 5.3 The Importance of XAI in Healthcare 5.4 Interpretable vs. Explainable AI 5.5 The Impact of Black Box AI in Healthcare 5.6 Case Studies of XAI in Healthcare 5.7 XAI Techniques 5.7.1 User-Centered Design for Explainable AI 5.8 Advantages and Limitations of Explainable AI 5.9 Challenges and Opportunities of XAI in Healthcare 5.10 Real-World Use Cases of Explainable AI in Healthcare 5.11 Future Trends 5.12 Conclusion References Section 2. Explainable AI in Smart Telemedicine and Telehealth Chapter 6 Sensor Scheduling in an IoT Health Monitoring System with Interference Awareness Abstract 6.1 Introduction 6.2 Internet of Things 6.3 IoT in Healthcare 6.4 Create a Framework for Monitoring Health Open-Source Hardware Sensor Connection for Application 6.5 Acquiring Information from Sensors 6.6 Suggested Method 6.7 Results 6.8 Conclusion References Chapter 7 A Time Series-Based Artificial Neural Networks for Predicting COVID-19 Positive Cases in Indonesia Abstract 7.1 Introduction 7.2 Literature Review 7.3 Proposed Methodology 7.3.1 Data Collection 7.3.2 Data Normalization 7.4 Artificial Neural Networks (ANNs) 7.5 ANNs Training Algorithms 7.5.1 Evaluation Metric Performance 7.6 Experimental Results 7.6.1 Results of ANN-LM 7.6.2 Results of ANN-BFGS 7.6.3 Results of ANN-SCG 7.7 Conclusion References Chapter 8 Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope Using Deep Learning Algorithm Abstract 8.1 Introduction 8.2 Types of Detection of Abnormal Sounds from Lungs Auscultation Pulmonary Function Tests (PFTs) Imaging Techniques Bronchoscope Blood Tests Summary 8.3 Literature Survey 8.3.1 Machine Learning (ML) Algorithms for Detecting Abnormal Sounds in Lungs 8.3.2 SVM for Abnormal Sound Detection in Lungs Using Vest Coat Stethoscope 8.3.3 Random Forest (RF) for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope 8.3.4 Decision Tree for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope The Decision Tree Approach Benefits of the Decision Tree Approach 8.3.5 K-Means Algorithm for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope Background K-Means Algorithm Application in Abnormal Sound Detection Benefits and Challenges 8.3.6 Linear Regression for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope Linear Regression for Abnormal Sound Detection Data Collection and Preprocessing Model Training and Evaluation Potential Challenges and Future Directions 8.4 Deep Learning Algorithms for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope: An Introduction 8.5 Proposed AI-Powered Vest-Coat (AI-VC) Dataset Description 8.6 CNN for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope Introduction Vest-Coat Stethoscope Convolutional Neural Network (CNN) for Abnormal Sound Detection Benefits and Potential Applications 8.7 Auto Encoder for Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope Limitations of Traditional Auscultation Proposed Solution Methodology Significance of the Study 8.8 Performance Metrics 8.9 Conclusion References Section 3. Public Health Application using Explainable AI Chapter 9 Heart Diseases Prediction Based on Multiple Machine Learning Models Abstract 9.1 Introduction 9.1.1 Classification 9.2 Background Study 9.3 Generalised Prediction and Analysis 9.4 Methodology 9.4.1. Metrics to Evaluate Performance 9.5. Experimental Setup 9.5.1. Data Description 9.5.2. Data Preprocessing 9.5.3. Supervised Machine Learning Algorithms Classification Techniques 9.5.3.1. Logistic Regression 9.5.3.2. Support Vector Machine 9.5.3.3. Naïve Bayes 9.5.3.4. Random Forest 9.5.4. Analysis Using Logistic Regression (LR) 9.5.5. Analysis Using Support Vector Machine (SVM) 9.5.6. Analysis Using Naïve Bayes (NV) 9.5.7. Analysis Using Random Forest (RF) 9.6. Prediction Comparative and Result Analysis among Different Models 9.7 Conclusion and Future Scope References Chapter 10 Artificial Intelligence and Explainable Artificial Intelligence Applications for Predicting and Preventing Complications in Maternal Health Abstract 10.1 Introduction 10.1.1. Overview of Pregnancy and Maternal Care 10.1.2. Artificial Intelligence and Machine Learning in Maternal Care 10.2. Applications of AI and ML in Pregnancy Risk Assessment 2.1. Estimation of Foetal Gestational Age 10.2.2. CRL Measurement Is Essential for Accurate Estimation of Gestational Age 10.2.3. Prediction of Foetal Growth Restriction 10.2.4. Detection of Congenital Anomalies 10.2.5. Prediction of Gestational Diabetes Mellitus 10.2.6. Prediction of Preeclampsia 10.2.7. Prediction of Preterm Labor 10.3. Applications of AI in Delivery and Postpartum 10.3.1. Real-Time Monitoring During Labor 10.3.2. Postpartum Haemorrhage 10.3.3. Breastfeeding Support 10.3.4. Postpartum Depression Prediction 10.4. Medication Safety 10.5. Clinical Decision System 10.6 Explainable Artificial Intelligence 10.7 Ethical and Legal Considerations 10.8 Challenges and Future Directions 10.9 Conclusion References Chapter 11 Heart Disease Prediction Using Machine Learning Algorithm with Explainable Artificial Intelligence for Health Care System Abstract 11.1 Introduction 11.2 Literature Survey 11.2.1. Background Work 11.2.2. Logistic Regression 11.2.3. Random Forest 11.2.4. K-Nearest Neighbour 11.2.5. Explainable Artificial Intelligence (XAI) Method LIME 11.3. Proposed Methodology 11.3.1. Pre-Processing 11.3.2. Classification Techniques 11.3.3. Description of the Output 11.3.4. Implementation and Testing 11.3.4.1. Dataset Description 11.4. Results 11.5 Conclusion References Chapter 12 Ailment Prophecy Based on Symptoms Using Machine Learning Abstract 12.1 Introduction 12.2 Literature Review 12.3 Proposed Methodology Module 1: Data Preparation Module 2: Building the Model Using Random Forest Classifier Module 4: Naive Bayes Model Construction Module 5: Support Vector Machine Model Construction Inferences 12.4 Performance Analysis 12.5 Conclusion Chapter 13 Predicting the Disease Outbreak Using Artificial Intelligence and Data Mining Techniques Abstract 13.1 Introduction 13.2 Literature Review 13.3 Methodology 13.3.1 Association Rule Mining Concept Associated Parameters Support Confidence Lift 13.3.2 FP Growth – Association Rule Mining Algorithm 13.3.3. Graph Node Classification Using Airline Routes Concept Creating the Graph Graph Node Embedding Algorithm - node2vec Algorithm Random Walks Skip-Gram Model Negative Sampling Optimization Embedding Extraction 13.3.4 Logistic Regression 13.4 Novelty of the Proposed Approach 13.5 Experimental Data, Implementation and Results Experimental Data Association Rule Mining Graph Node Classification Disease Dataset Airline Routes Airport IATA Code - Country Country Geographical Coordinates 13.6 Implementation FP Growth Algorithm-Association Rule Mining Preprocessing Applying the FP Growth Algorithm Generating Predictions Graph node Classification Preprocessing Graph Formation Graph Node Embedding Logistic Regression 13.7 Evaluation Accuracy Precision 13.8 Results Association Rule Mining Graph Node Classification 13.9 Discussion and Conclusion References Chapter 14 Explainable AI in Medical Image Processing for Health Care Abstract 14.1 Introduction 14.2 Explainable AI in Medical Image Processing 14.2.1 Major Challenges in XAI 14.2.2 Benefits of Explainable AI in Medical Image Processing 14.3 Classification Models Decision Tree Naive Bayes Random Forest Classifier Model A Support Vector Machine (SVM) 14.4 Literature Review 14.5 Exploratory Data Analysis Data Cleaning 14.6 Explainable AI Techniques in Medical Image Processing 14.7 Medical Image Classification Local Interpretable Model-Agnostic Explanations (LIME) Grad-Cam Proposed Methodology Performance Measures in XAI 14.8 Experimental Result and Analysis Datasets 14.8.1 Interpretability by LIME Results of GradCam 14.9 Conclusion References Chapter 15 XAI in Health Care for Making Intelligent Decisions for COVID Prevention and Detection Abstract 15.1 Introduction 15.2 Framework from Machine Learning XAI 15.2.1 Linear Regression Applications Advantages Disadvantages 15.2.2 Random Forest Uses of Random Forest Applications of Random Forest Steps for the Random Forest Algorithm: Advantages Discussion on Random Forest 15.3 COVID Data Set Description 15.3.1 Data Pre-Processing The Major Steps for Data Pre-Processing Knowledge Representation of XAI Interpretability and Explainability Explainability 15.4 XAI Approaches and Analysis in Sustainable Smart Health Care Informatics Characteristics of Explainable AI in Healthcare 15.5 XAI in Threats Challenges in XAI 15.6 Experimental Analysis 15.7 Opportunities in XAI 15.8 Conclusion References Section 4. Medical Imaging Classification using Explainable AI Chapter 16 Explainable AI in Healthcare Abstract 16.1 Introduction 16.1.1 Interpretable AI 16.1.2 Transparent AI 16.1.3 Interactable AI Objective of Study 16.2 General Process of XAI 16.3 Literature Survey 16.4 Proposed Approach with COVID-19 and Pneumonia Case Study 16.5 Problems Associated with Current XAI Techniques 16.6 XAI Recommandations System 16.7 Conclusion References Chapter 17 Medical Image-Based Steganography Using Deoxyribonucleic Acid (DNA) Algorithm Abstract 17.1 Introduction 17.2 Literature Review 17.3 Proposed method Embedding Procedure 17.4 The Process of this Proposed Model Takes Place in the Following Sequence of Steps STEP 1 - DNA Encoding Step 2 - Algorithmic Selector Step 3 - LSB Substitution Method Decoding Procedure 17.5 Experimental Results Analysis 17.6 Stego-Images Quality Analysis Robustness Analysis 17.7 Comparative Analysis 17.8 Conclusion References Chapter 18 Deep Convolutional Neural Network for Classifying COVID-19 and Pneumonia Using Chest X-Ray Images Abstract 18.1 Introduction 18.2 Related Works 18.3 Materials and Methods 18.3.1 Dataset Description 18.3.2 Convolutional Neural Network (CNN) Convolution Layer Pooling Layer Fully Connected Layer Output Layer Pre-Trained Models ResNet50 and ResNet101 VGG16 and VGG19 18.4 Performance Matrix for Classification 18.5 Results and Discussion 18.6 Conclusion References Chapter 19 XAI-Driven Visualization for Improved Decision-Making in Sustainable and Smart Healthcare Abstract 19.1 Introduction 19.2 XAI-Driven Visualisation Techniques for Healthcare 19.2.1 Line and Area Charts 19.2.2 Bar Charts and Histogram 19.2.3 Scatter Plots 19.2.4 Heatmaps 19.3 Geographic Information System Mapping 19.3.1 Interactive Dashboard 19.3.2 Decision Tree 19.4 Applications of XAI-Driven Visualization in Sustainable Healthcare 19.4.1 Visualization for Sustainable Supply Chain Management 19.4.2 Visual Analytics for Sustainable Healthcare Planning 19.4.3 Applications of XAI-Driven Visualization in Smart Healthcare 19.4.4 Real-Time Monitoring and Visualization of Vital Signs 19.4.5 Visual Analytics for Population Health Management 19.5 Challenges and Considerations in Implementing XAI-Driven Visualization 19.5.1 Data Quality and Availability 19.5.2 Design and Usability 19.5.3 Privacy and Security 19.5.4 Ethical Considerations 19.6 Discussion 19.7 Conclusion and Future Work References Chapter 20 Threats, Difficulties and Possibilities for XAI in Healthcare Decision Support Systems Abstract 20.1. Introduction 20.2. Fundamental Concepts and Background 20.2.1. Healthcare Decision Support Systems 20.2.2. Explainable AI (XAI) 20.2.2.1. The Need for XAI : Fair and Ethical Decision-Making 20.2.3. XAI in Medicine 20.2.4. Techniques and Explanation 20.2.4.1. Ante-Hoc Methods 20.3. Materials and Methods 20.3.1. Research Questions 20.4. Results 20.4.1. What AI-Based HDSS Have Been Developed That Incorporates XAI? 20.4.2. What Benefits Have Been Reported When Addressing Different Aspects of the Use of XAI in HDSS? 20.4.3. Is HDSS Expressed in Literature? 20.5. Discussion 20.5.1. Guidelines for Implementing Explainable Models in HDSS: Difficulties, Possibilities, and Future Research Needs 20.6. Conclusion References About the Editors Index Blank Page