Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.
In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.
This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.
1. Machine Learning, Big Data, and IoT for Medical Informatics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. Preface - p. Page vii 8. List of Illustrations - p. Page xi 9. List of Tables - p. Page xii 10. Chapter 1 : Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques - p. Page xii 11. Chapter 2 : Geolocation-aware IoT and cloud-fog-based solutions for healthcare - p. Page 23 12. Chapter 3 : Machine learning vulnerability in medical imaging - p. Page 51 13. Chapter 4 : Skull stripping and tumor detection using 3D U-Net - p. Page 67 14. Chapter 5 : Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach - p. Page 82 15. Chapter 6 : Estimating the respiratory rate from ECG and PPG using machine learning techniques - p. Page 94 16. Chapter 7 : Machine learning-enabled Internet of Things for medical informatics - p. Page 109 17. Chapter 8 : Edge detection-based segmentation for detecting skin lesions - p. Page 124 18. Chapter 9 : A review of deep learning approaches in glove-based gesture classification - p. Page 138 19. Chapter 10 : An ensemble approach for evaluating the cognitive performance of human population at high altitude - p. Page 161 20. Chapter 11 : Machine learning in expert systems for disease diagnostics in human healthcare - p. Page 175 21. Chapter 12 : An entropy-based hybrid feature selection approach for medical datasets - p. Page 196 22. Chapter 13 : Machine learning for optimizing healthcare resources - p. Page 212 23. Chapter 14 : Interpretable semisupervised classifier for predicting cancer stages - p. Page 235 24. Chapter 15 : Applications of blockchain technology in smart healthcare: An overview - p. Page 256 25. Chapter 16 : Prediction of leukemia by classification and clustering techniques - p. Page 272 26. Chapter 17 : Performance evaluation of fractal features toward seizure detection from electroencephalogram signals - p. Page 285 27. Chapter 18 : Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences - p. Page 300 28. Chapter 19 : A blockchain solution for the privacy of patients’ medical data - p. Page 314 29. Chapter 20 : A novel approach for securing e-health application in a cloud environment - p. Page 346 30. Chapter 21 : An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm - p. Page 362 31. Chapter 22 : A review of deep learning models for medical diagnosis - p. Page 383 32. Chapter 23 : Machine learning in precision medicine - p. Page 401 33. Index - p. Page 416
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.
In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.
This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.
1. Machine Learning, Big Data, and IoT for Medical Informatics - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. Preface - p. Page vii 8. List of Illustrations - p. Page xi 9. List of Tables - p. Page xii 10. Chapter 1 : Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques - p. Page xii 11. Chapter 2 : Geolocation-aware IoT and cloud-fog-based solutions for healthcare - p. Page 23 12. Chapter 3 : Machine learning vulnerability in medical imaging - p. Page 51 13. Chapter 4 : Skull stripping and tumor detection using 3D U-Net - p. Page 67 14. Chapter 5 : Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach - p. Page 82 15. Chapter 6 : Estimating the respiratory rate from ECG and PPG using machine learning techniques - p. Page 94 16. Chapter 7 : Machine learning-enabled Internet of Things for medical informatics - p. Page 109 17. Chapter 8 : Edge detection-based segmentation for detecting skin lesions - p. Page 124 18. Chapter 9 : A review of deep learning approaches in glove-based gesture classification - p. Page 138 19. Chapter 10 : An ensemble approach for evaluating the cognitive performance of human population at high altitude - p. Page 161 20. Chapter 11 : Machine learning in expert systems for disease diagnostics in human healthcare - p. Page 175 21. Chapter 12 : An entropy-based hybrid feature selection approach for medical datasets - p. Page 196 22. Chapter 13 : Machine learning for optimizing healthcare resources - p. Page 212 23. Chapter 14 : Interpretable semisupervised classifier for predicting cancer stages - p. Page 235 24. Chapter 15 : Applications of blockchain technology in smart healthcare: An overview - p. Page 256 25. Chapter 16 : Prediction of leukemia by classification and clustering techniques - p. Page 272 26. Chapter 17 : Performance evaluation of fractal features toward seizure detection from electroencephalogram signals - p. Page 285 27. Chapter 18 : Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences - p. Page 300 28. Chapter 19 : A blockchain solution for the privacy of patients’ medical data - p. Page 314 29. Chapter 20 : A novel approach for securing e-health application in a cloud environment - p. Page 346 30. Chapter 21 : An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm - p. Page 362 31. Chapter 22 : A review of deep learning models for medical diagnosis - p. Page 383 32. Chapter 23 : Machine learning in precision medicine - p. Page 401 33. Index - p. Page 416
