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Machine Learning, Big Data, and IoT for Medical Informatics de Pardeep Kumar

Machine Learning, Big Data, and IoT for Medical Informatics: Intelligent Data-Centric Systems

Editat de Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid Fatos Xhafa
en Limba Engleză Paperback – 15 iun 2021
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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Specificații

ISBN-13: 9780128217771
ISBN-10: 0128217774
Pagini: 458
Ilustrații: Approx. 110 illustrations
Dimensiuni: 191 x 235 x 30 mm
Greutate: 0.78 kg
Editura: ELSEVIER SCIENCE
Seria Intelligent Data-Centric Systems

Public țintă

Healthcare engineers, informaticians, bio-statisticians, researchers, graduate students, data analyst, physician, and any other person having interest in the field of medical informatics. Scientist, researchers, practitioners, and academicians in higher education institutions including universities and vocational colleges.

Cuprins

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