Explainable Artificial Intelligence in Healthcare Systems / edited by A. Anitha Kamaraj, Debi Prasanna Acharjya.

Format:

Book

Contributor:

Kamaraj, A. Anitha, editor.

Acharjya, D. P., 1969- editor.

Series:

Computer science, technology and applications.

Computer Science, Technology and Applications Series

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (389 pages)

Edition:

First edition.

Place of Publication:

New York : Nova Science Publishers, Inc., [2024]

Summary:

"The twenty chapters in this book offers a comprehensive overview of the theory, algorithms, and applications of interpretable and XAI, as well as recent advances in the field of healthcare, reflecting the current discourse and offering suggestions for future research. The book is divided into four sections: primitive concepts of XAI, XAI in smart Telemedicine and Tele health, public health application using XAI and medical imaging classification using XAI. Thus, the book covers a comprehensive set of material ranging from fundamentals to image analysis employing XAI ideas"-- Provided by publisher.

Contents:

Intro

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

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

Chapter 3

XAI in Healthcare: A SWOT Analysis

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

Chapter 4

A Comprehensive Review on the Developments in Explainable Artificial Intelligence

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

Chapter 5

Unveiling the Algorithms: How Explainable AI Reshapes Healthcare

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

Section 2. Explainable AI in Smart Telemedicine and Telehealth

Chapter 6

Sensor Scheduling in an IoT Health Monitoring System with Interference Awareness

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

Chapter 7

A Time Series-Based Artificial Neural Networks for Predicting COVID-19 Positive Cases in Indonesia

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

Chapter 8

Abnormal Sound Detection in Lungs Using Vest-Coat Stethoscope Using Deep Learning Algorithm

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

Section 3. Public Health Application using Explainable AI

Chapter 9

Heart Diseases Prediction Based on Multiple Machine Learning Models

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

Chapter 10

Artificial Intelligence and Explainable Artificial Intelligence Applications for Predicting and Preventing Complications in Maternal Health

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

Chapter 11

Heart Disease Prediction Using Machine Learning Algorithm with Explainable Artificial Intelligence for Health Care System

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

Chapter 12

Ailment Prophecy Based on Symptoms Using Machine Learning

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references and index.

ISBN:

979-88-911-3667-0