Description based upon print version of record.

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Chapter 1: Introduction to Artificial Intelligence in Healthcare -- 1.1 Introduction -- 1.1.1 Major Activities in Medical Image Analysis -- 1.1.2 The Role of ML in Medical Image Analysis -- 1.2 Medical Imaging Types -- 1.2.1 Pre-Processing Using ML -- 1.2.1.1 Introduction to Pre-processing -- 1.2.2 Segmentation Using ML -- 1.2.2.1 Introduction to Segmentation -- 1.2.3 Registration Using ML -- 1.2.3.1 Introduction -- 1.3 Deep Learning in Medical Imaging -- 1.4 Conclusion -- References

Chapter 2: Machine Learning in Radio Imaging -- 2.1 Introduction -- 2.2 Analysis of Related Work -- 2.3 Summary -- References -- Chapter 3: Solutions Using Machine Learning for Diabetes -- 3.1 Introduction -- 3.2 Diabetes Prevalence -- 3.3 Diabetes Risk Factors -- 3.4 Machine Learning -- 3.4.1 Artificial Neural Networks -- 3.4.2 Support Vector Machine -- 3.4.3 Fuzzy Logic -- 3.4.4 Logistic Regression -- 3.5 A Case Study -- 3.6 Results and Discussion -- 3.6.1 Multicollinearity Test Results/ Variables -- 3.6.2 Multilinear Regression Test Results / Coefficients -- 3.7 Conclusions -- References

Chapter 4: A Highly Reliable Machine Learning Algorithm for Cardiovascular Disease Prediction -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Methodology -- 4.3.1 Dataset -- 4.3.2 Implementing the Classifiers -- 4.3.2.1 XGBOOST -- 4.3.2.1.1 XGBoost Result Analysis -- 4.3.2.2 Random Forest -- 4.3.2.2.1 Random Forest Result Analysis -- 4.3.2.3 Naïve Bayes -- 4.3.2.3.1 Naïve Bayes Result Analysis -- 4.3.2.4 Majority Voting Ensemble (MVE) -- 4.3.2.4.1 Majority Voting Ensemble Result Analysis -- 4.4 Conclusion -- References -- Chapter 5: Machine Learning Algorithms for Industry Using Image Sensing

5.1 Introduction -- 5.2 What is Manufacturing Artificial Intelligence? -- 5.3 Defining the Industrial Internet of Things -- 5.4 IoT History -- 5.5 IIoT Architectures -- 5.6 Applications of IIoT -- 5.6.1 Smart Manufacturing -- 5.7 Securing the Internet of Things -- 5.7.1 Safety -- 5.7.2 Security -- 5.7.3 Privacy -- 5.8 Challenges and Opportunities -- 5.9 Future of IIoT -- 5.10 Communication 5G and Beyond -- 5.11 How Did AI Develop in Production? -- 5.11.1 AI in Manufacturing Current State -- 5.11.2 What is AI's Future in Production?

5.12 Process and Factory Floors that are Flexible and Configurable -- 5.13 Manufacturing and AI: Applications and Benefits -- 5.14 Impact of COVID-19 on Industrial Internet of Things -- 5.15 Conclusion -- References -- Chapter 6: Solutions Using Machine Learning for COVID-19 -- 6.1 Introduction -- 6.2 Application of ML and AI Methods to COVID-19 -- 6.2.1 Screening and Diagnosis -- 6.2.1.1 Using the Patient's Symptoms and Routine Tests -- 6.2.1.2 Using Chest X-rays and CT Images -- 6.2.2 Population Monitoring -- 6.2.2.1 Monitoring Patients -- 6.2.2.2 Contact Tracing

6.2.2.3 Avoiding Physical Contact

Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence. The wide variety of topics covered include: Artificial Intelligence in healthcare Machine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19 Big data analytics solutions for healthcare data processing Reliable biomedical applications using AI models Intelligent IoT in healthcare The book explains fundamental concepts as well as the advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, anddata informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain.

OCLC-licensed vendor bibliographic record.