Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
- Length: 264 pages
- Edition: 1
- Language: English
- Publisher: Medical Information Science Reference
- Publication Date: 2021-06-25
- ISBN-10: 1799871886
- ISBN-13: 9781799871880
- Sales Rank: #0 (See Top 100 Books)
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 Mission Coverage Editorial Advisory Board Preface Acknowledgment Chapter 1: Prediction of Neurological Disorders Using Visual Saliency ABSTRACT INTRODUCTION BACKGROUND RELATED WORK PREDICTION OF NEUROLOGICAL DISORDERS USING VISUAL SALIENCY SOME POTENTIAL RESEARCH DIMENSIONS CONCLUSION REFERENCES Chapter 2: An Exploratory Analysis and Predictive SIR Model for the Early Onset of COVID-19 in Tamil Nadu, India ABSTRACT INTRODUCTION BACKGROUND ANALYSIS FOR TAMIL NADU DISCUSSIONS FUTURE RESEARCH SCOPE CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS Chapter 3: Predicting Daily Confirmed COVID-19 Cases in India ABSTRACT INTRODUCTION ARIMA MODEL METHODOLOGY ESTIMATING THE MODEL CONCLUSION AND DISCUSSION REFERENCES Chapter 4: A Study on COVID-19 Prediction and Detection With Artificial Intelligence-Based Real-Time Healthcare Monitoring Systems ABSTRACT OBJECTIVES OF THIS REVIEW INTRODUCTION ARTIFICIAL INTELLIGENCE INITIATION ROLE OF ARTIFICIAL INTELLIGENCE-BASED TECHNIQUES IN THE HEALTH SECTOR ROLE OF ARTIFICIAL INTELLIGENCE-BASED MACHINE LEARNING TECHNIQUES IN THE HEALTH SECTOR SUPERVISED, UNSUPERVISED MACHINE LEARNING, DEEP LEARNING METHODS USED TO PREDICTION MAINTAIN A REAL-TIME DATABASE OF COVID-19 IN GITHUB, GOOGLE DRIVE THROUGH AI TECHNIQUES DIAGNOSE COVID-19 WITH COMPUTER TOMOGRAPHY BASED ON AI TECHNIQUES DEEP LEARNING MODEL USED TO IDENTIFY COVID-19 AND PNEUMONIA CONCLUSION REFERENCES Chapter 5: Landmark Recognition Using Ensemble-Based Machine Learning Models ABSTRACT INTRODUCTION MACHINE LEARNING AND ITS TECHNIQUES IMAGE CLASSIFICATION TECHNIQUES TECHNIQUES FOR LANDMARK RECOGNITION IMPLEMENTATION OF SOME ML TECHNIQUES FUTURE SCOPE CONCLUSION REFERENCES Chapter 6: Image Classification Using Deep Neural Networks ABSTRACT INTRODUCTION STUDY OF LITERATURE REQUIREMENT FOR THE STUDY IMPLEMENTATION PROCESS RESULT AND ANALYSIS CONCLUSION REFERENCES Chapter 7: Pandemic Management Using Artificial Intelligence-Based Safety Measures ABSTRACT INTRODUCTION ARTIFICIAL INTELLIGENCE TO CHALLENGE COVID-19 OUTBREAK COMPUTER VISION FOR COVID-19 DEEP LEARNING MODEL FOR COVID -19 MACHINE LEARNING TECHNIQUES FOR COVID- 19 METHODS DISCUSSION CONCLUSION REFERENCES Chapter 8: Text Mining and Natural Language Processing for Health Informatics ABSTRACT INTRODUCTION STATE-OF-THE-ART IN HEALTH INFORMATICS USING NATURAL LANGUAGE PROCESSING AND TEXT MINING NATURAL LANGUAGE PROCESSING AND TEXT MINING AS AN ENABLER FOR HEALTH INFORMATICS NLP AND TEXT MINING IN HEALTH INFORMATICS: FUTURE RESEARCH DIMENSIONS CONCLUSION REFERENCES Chapter 9: Plant Disease Detection Using Machine Learning Approaches ABSTRACT INTRODUCTION LITERATURE SURVEY PROCESS FOR PLANT DISEASE DETECTION CHALLENGES CONCLUSION REFERENCES Chapter 10: Image Pre-Processing and Paddy Pests Detection Using Tensorflow ABSTRACT INTRODUCTION IMAGE VARIATIONS AND PRE-PROCESSING METHODS STEPS FOR LABELING IMAGES IMAGE CLASSIFICATION USING TENSORFLOW REFERENCES Chapter 11: Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease ABSTRACT INTRODUCTION ALZHEIMER’S DISEASES RECURRENT NEURAL NETWORKS AND IMAGE PROCESSING COMPUTER VISION AND IMAGE PROCESSING CNN AND MEDICAL SCIENCES TRANSFER LEARNING AND MEDICAL SCIENCE BLOCKCHAIN AND MEDICAL SCIENCE CONCLUSION REFERENCES Chapter 12: Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms ABSTRACT INTRODUCTION BACKGROUND MACHINE LEARNING ALGORITHMS LITERATURE REVIEW ROC AND AUC CONCLUSION OF THE CASE STUDY FUTURE SCOPE DISEASE CONDITION MONITORING CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS Chapter 13: Criticality of E-Privacy and Data Leakage Amid the Pandemic ABSTRACT INTRODUCTION THE DATA FLOW DATA BREACH AND E-BEHAVIOR LEAKAGE DATA PRIVACY PREVENTION TECHNIQUES ANONYMITY IN DATA PUBLISHING AND MINING CONCLUSION REFERENCES Chapter 14: Designing a Real-Time Dashboard for Pandemic Management ABSTRACT COVID -19 OUTBREAK GROWTH OF COVID-19 FROM ORIGIN TO 1ST LOCKDOWN IMPACT ON ECONOMY SECOND WAVE OF COVID-19 VACCINE AND ITS DISTRIBUTION PRESENT SITUATION COMPARED TO COVID - 19 OUTBREAK CONCLUSION REFERENCES Chapter 15: Corona ABSTRACT INTRODUCTION BACKGROUND HISTORY EMERGENCE OF CORONA VIRUS HOW HAS CORONA AFFECTED THE NATIONS? PENETRATION INSIDE THE HUMAN BODY SYMPTOMS IN THE HUMAN BODY TREATMENT OF THE DISEASE PRECAUTIONS TO PREVENT THE SPREAD OF A DISEASE CORONA VIRUS VARIANTS FUTURE WITH CORONA VIRUS REFERENCES Compilation of References About the Contributors
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