Machine Learning, Big Data, and IoT for Medical Informatics

Machine Learning, Big Data, and IoT for Medical Informatics – Ebook Instant Download/Delivery ISBN(s): 9780128217771,0128217774,9780128217818, 0128217812

Product detail:

• ISBN 10: 0128217812
• ISBN 13: 9780128217818
• Author:  Academic Press

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.

Table of contents:

1. Chapter 1: Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
2. Chapter 2: Geolocation-aware IoT and cloud-fog-based solutions for healthcare
3. Chapter 3: Machine learning vulnerability in medical imaging
4. Chapter 4: Skull stripping and tumor detection using 3D U-Net
5. Chapter 5: Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach
6. Chapter 6: Estimating the respiratory rate from ECG and PPG using machine learning techniques
7. Chapter 7: Machine learning-enabled Internet of Things for medical informatics
8. Chapter 8: Edge detection-based segmentation for detecting skin lesions
9. Chapter 9: A review of deep learning approaches in glove-based gesture classification
10. Chapter 10: An ensemble approach for evaluating the cognitive performance of human population at high altitude
11. Chapter 11: Machine learning in expert systems for disease diagnostics in human healthcare
12. Chapter 12: An entropy-based hybrid feature selection approach for medical datasets
13. Chapter 13: Machine learning for optimizing healthcare resources
14. Chapter 14: Interpretable semisupervised classifier for predicting cancer stages
15. Chapter 15: Applications of blockchain technology in smart healthcare: An overview
16. Chapter 16: Prediction of leukemia by classification and clustering techniques
17. Chapter 17: Performance evaluation of fractal features toward seizure detection from electroencephalogram signals
18. Chapter 18: Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences
19. Chapter 19: A blockchain solution for the privacy of patients’ medical data
20. Chapter 20: A novel approach for securing e-health application in a cloud environment
21. Chapter 21: An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
22. Chapter 22: A review of deep learning models for medical diagnosis
23. Chapter 23: Machine learning in precision medicine

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