Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.
This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.
1. Section 1: Introduction to artificial intelligence - p. xx 2. Chapter 1. Data processing - p. 3 3. Chapter 2. Regression, classification, and clustering algorithms - p. 23 4. Chapter 3. Deep learning - p. 63 5. Section 2: Application of artificial intelligence in disease diagnosis - p. 87 6. Chapter 4. Application of artificial intelligence in pioneering heart disease detection - p. 93 7. Chapter 5. From data to diagnosis: leveraging machine learning for heart disease classification - p. 115 8. Chapter 6. Artificial intelligence–based treatment solutions - p. 129 9. Chapter 7. Application of artificial intelligence in big data management - p. 145 10. Section 3: AI in health monitoring and wearable devices - p. 153 11. Chapter 8. Remote health monitoring using artificial intelligence - p. 159 12. Chapter 9. Predicting women’s fertility with artificial intelligence - p. 183 13. Chapter 10. A comparative study on “face mask detection” using machine learning and deep learning algorithms - p. 193 14. Chapter 11. Enhancing communication: a review on applications of AI–based wearables for the deaf and mute - p. 201 15. Chapter 12. Digital sphygmomanometric measurement system for patients with chronic illnesses based on artificial intelligence - p. 217 16. Section 4: Application of AI medical image recognition - p. 223 17. Chapter 13. Identifying cardiovascular abnormalities - p. 227 18. Chapter 14. Nonlinear activation functions of convolutional neural networks for classification of brain tumors using magnetic resonance imaging - p. 245 19. Chapter 15. Artificial intelligence–based management prospects of neurological disorders with special reference to epilepsy - p. 283 20. Chapter 16. Screening for common cancers - p. 297 21. Index - p. 309
Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of machine and deep learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.
This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.
1. Section 1: Introduction to artificial intelligence - p. xx 2. Chapter 1. Data processing - p. 3 3. Chapter 2. Regression, classification, and clustering algorithms - p. 23 4. Chapter 3. Deep learning - p. 63 5. Section 2: Application of artificial intelligence in disease diagnosis - p. 87 6. Chapter 4. Application of artificial intelligence in pioneering heart disease detection - p. 93 7. Chapter 5. From data to diagnosis: leveraging machine learning for heart disease classification - p. 115 8. Chapter 6. Artificial intelligence–based treatment solutions - p. 129 9. Chapter 7. Application of artificial intelligence in big data management - p. 145 10. Section 3: AI in health monitoring and wearable devices - p. 153 11. Chapter 8. Remote health monitoring using artificial intelligence - p. 159 12. Chapter 9. Predicting women’s fertility with artificial intelligence - p. 183 13. Chapter 10. A comparative study on “face mask detection” using machine learning and deep learning algorithms - p. 193 14. Chapter 11. Enhancing communication: a review on applications of AI–based wearables for the deaf and mute - p. 201 15. Chapter 12. Digital sphygmomanometric measurement system for patients with chronic illnesses based on artificial intelligence - p. 217 16. Section 4: Application of AI medical image recognition - p. 223 17. Chapter 13. Identifying cardiovascular abnormalities - p. 227 18. Chapter 14. Nonlinear activation functions of convolutional neural networks for classification of brain tumors using magnetic resonance imaging - p. 245 19. Chapter 15. Artificial intelligence–based management prospects of neurological disorders with special reference to epilepsy - p. 283 20. Chapter 16. Screening for common cancers - p. 297 21. Index - p. 309
