Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease

Description

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students. Cover Title Page Copyright Page Book Series Editorial Advisory Board Table of Contents Detailed Table of Contents Preface Acknowledgment Chapter 1: Prediction of Neurological Disorders Using Visual Saliency Chapter 2: An Exploratory Analysis and Predictive SIR Model for the Early Onset of COVID-19 in Tamil Nadu, India Chapter 3: Predicting Daily Confirmed COVID-19 Cases in India Chapter 4: A Study on COVID-19 Prediction and Detection With Artificial Intelligence-Based Real-Time Healthcare Monitoring Systems Chapter 5: Landmark Recognition Using Ensemble-Based Machine Learning Models Chapter 6: Image Classification Using Deep Neural Networks Chapter 7: Pandemic Management Using Artificial Intelligence-Based Safety Measures Chapter 8: Text Mining and Natural Language Processing for Health Informatics Chapter 9: Plant Disease Detection Using Machine Learning Approaches Chapter 10: Image Pre-Processing and Paddy Pests Detection Using Tensorflow Chapter 11: Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease Chapter 12: Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms Chapter 13: Criticality of E-Privacy and Data Leakage Amid the Pandemic Chapter 14: Designing a Real-Time Dashboard for Pandemic Management Chapter 15: Corona Compilation of References About the Contributors Index