Machine Learning, Big Data, and IoT for Medical Informatics

Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid, Fatos Xhafa

• Publisher: Academic Press
• Year: 2021
• Print ISBN: 9780128217771
• eBook ISBN: 9780128217818
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

Table of contents

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