Kumar P. Machine Learning, Big Data, and IoT for Medical Informatics 2021

Textbook in PDF format

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