Machine Learning, Big Data, and IoT for Medical Informatics
Published on 13. June 2021
458 pages
978-0-12-821781-8 (ISBN)
Description
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.
- Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems.
- Includes several privacy preservation techniques for medical data.
- Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis.
- Offers case studies and applications relating to machine learning, big data, and health care analysis.
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Additional editions
Pardeep Kumar | Yugal Kumar | Mohamed A. Tawhid
Machine Learning, Big Data, and IoT for Medical Informatics
Book
06/2021
Academic Press
€124.50
Shipment within 15-20 days
Content
1. Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques2. Geolocation-aware IoT and cloud-fog-based solutions for healthcare3. Machine learning vulnerability in medical imaging4. Skull stripping and tumor detection using 3D U-Net5. Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach6. Estimating the respiratory rate from ECG and PPG using machine learning techniques7. Machine learning-enabled Internet of Things for medical informatics8. Edge detection-based segmentation for detecting skin lesions9. A review of deep learning approaches in glove-based gesture classification10. An ensemble approach for evaluating the cognitive performance of human population at high altitude11. Machine learning in expert systems for disease diagnostics in human healthcare12. An entropy-based hybrid feature selection approach for medical datasets13. Machine learning for optimizing healthcare resources14. Interpretable semi-supervised classifier for predicting cancer stages15. Applications of blockchain technology in smart healthcare: An overview16. Prediction of leukemia by classification and clustering techniques17. Performance evaluation of fractal features toward seizure detection from electroencephalogram signals18. Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences19. A blockchain solution for the privacy of patients' medical data20. A novel approach for securing e-health application in a cloud environment21. An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm22. A review of deep learning models for medical diagnosis23. Machine learning in precision medicine
