System Design for Epidemics Using Machine Learning and Deep Learning

336 Pages·2023·16.05 MB·English

by  G. R. Kanagachidambaresan, Dinesh Bhatia, Dhilip Kumar, Animesh Mishra

Signals and Communication Technology
G. R. Kanagachidambaresan
Dinesh Bhatia
Dhilip Kumar
Animesh Mishra   Editors
System Design
for Epidemics
Using Machine
Learning and
Deep Learning
Signals and Communication Technology
Series Editors
Emre Celebi, Department of Computer Science
University of Central Arkansas
Conway, AR, USA
Jingdong Chen, Northwestern Polytechnical University
Xi’an, China
E. S. Gopi, Department of Electronics and Communication Engineering
National Institute of Technology
Tiruchirappalli, Tamil Nadu, India
Amy Neustein, Linguistic Technology Systems
Fort Lee, NJ, USA
H. Vincent Poor, Department of Electrical Engineering
Princeton University
Princeton, NJ, USA
Antonio Liotta, University of Bolzano
Bolzano, Italy
Mario Di Mauro, University of Salerno
Salerno, Italy
This series is devoted to fundamentals and applications of modern methods of signal
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contact Mary James at [email protected] or Ramesh Nath Premnath at
[email protected].
G. R. Kanagachidambaresan
Dinesh Bhatia • Dhilip Kumar • Animesh Mishra
Editors
System Design
for Epidemics Using
Machine Learning and
Deep Learning
Editors
G. R. Kanagachidambaresan Dinesh Bhatia
Department of CSE Department of Biomedical Engineering
Vel Tech Rangarajan Dr Sagunthala R&D North Eastern Hill University
Institute of Science and Technology Shillong, Meghalaya, India
Chennai, India
Animesh Mishra
Visiting Associate Professor
North Eastern Indira Gandhi Regional
Department of Institute of
Institute
Intelligent Systems
Shillong, India
University of Johannesburg
South Africa
∙
Dhilip Kumar
Computer Science and Engineering
Vel Tech Rangarajan Dr. Sagunthala R&D,
Institute of Science and Technology
Chennai, TN, India
ISSN 1860-4862 ISSN 1860-4870 (electronic)
Signals and Communication Technology
ISBN 978-3-031-19751-2 ISBN 978-3-031-19752-9 (eBook)
https://doi.org/10.1007/978-3-031-19752-9
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Preface
The World Health Organization (WHO) proclaimed a COVID-19 pandemic in
March 2020 (WHO 2020). COVID-19 is a multi-system disorder caused by the
SARS-CoV-2 coronavirus. Vaccine development was prioritized for managing and
controlling the pandemic due to concerns about the disease’s spread and severity, as
well as the effectiveness of available therapies. During epidemics or pandemics,
such as the current Coronavirus Disease 2019 (COVID-19) crisis, healthcare prac-
titioners (HCPs) face a variety of difficult situations. The stress of treating patients
during an epidemic or pandemic may be detrimental to HCPs’ mental health.
Evidence-based recommendations on what would be useful in reducing this impact
are lacking. Digital healthcare has been a hot topic among people for quite a while.
The pre/post pandemic situations have led patients and subjects to be monitored
outside the hospital environment with more digital equipment to avoid spreading of
COVID. Tapping into the available data to extract useful information is a challenge
that’s starting to be met using the pattern matching abilities of machine learning
(ML) – a subset of the field of artificial intelligence (AI). In order to provide smarter
environments, machine learning needs to be implemented in the Internet of Things
(IoT). Machine learning will allow these smart devices to become smarter in a lit-
eral sense. It can analyze the data generated by the connected devices and get an
insight into the human’s behavioral pattern. Without implementing ML, it would
really be difficult for smart devices and the IoT-based digital healthcare to make
smart decisions in real time, severely limiting their capabilities. This book provides
the challenges and the possible solutions in these areas. Various image-based and
data-based artificial intelligence approaches are discussed in this book to improve
the healthcare services in hospital.
Chennai, India G. R. Kanagachidambaresan
Shillong, India Dinesh Bhatia
Dhilip Kumar
Animesh Mishra
vii
Acknowledgments
We thank all authors, who have put a lot of time effort and their findings in this book
project. We would like to thank our reviewers for providing timely reviews. The
support from Vel Tech, North Eastern Hill University, and North Eastern Indira
Gandhi Regional Institute of Health and Medical Sciences allowed editing and sup-
port of our projects. We hope this book will be a valuable material for all healthcare
researchers.
ix
Contents
Pandemic Effect of COVID-19: Identification,
Present Scenario, and Preventive Measures Using
Machine Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
A. Nazar Ali, Jai Ganesh, A. T. Sankara Subramanian, L. Nagarajan,
and G. R. Subhashree
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Coronavirus First Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Corona Cases in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Structure of Coronavirus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Growing Stages of Virus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Statistical Data of COVID-19 at Present Scenario . . . . . . . . . . . . . . . . . . . . . 5
COVID-19: Confirmed Cases and Causalities . . . . . . . . . . . . . . . . . . . . . . 5
COVID-19: Transmission Rate and Statistics on Different
Stages of Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
COVID-19: Case Fatality Rate (CFR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
COVID-19: Category-Wise Fatal Cases – Age Factor
and Medical History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Machine Learning Model for COVID-19 Prediction . . . . . . . . . . . . . . . . . . . 10
Baseline Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Training Using Unbiased Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Development of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Evaluation of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Transmission and Prevention of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Transmission of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Prevention and Control Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
xi
xii Contents
A Comprehensive Review of the Smart Health Records
to Prevent Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Kirti Verma, Neeraj Chandnani, Adarsh Mangal,
and M. Sundararajan
Introduction to a Smart Health Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Various Types of Records in Smart Health . . . . . . . . . . . . . . . . . . . . . . . . . 21
Development of EHR Standards for India . . . . . . . . . . . . . . . . . . . . . . . . . 22
Traditional Paper Records vs Smart Health Records . . . . . . . . . . . . . . . . . 23
Comparison Between Paper-Based Records and Electronic Health Records . 24
Interoperability and Standards in the Smart Healthcare System . . . . . . . . 25
Guidelines for Proposed Smart Health Records . . . . . . . . . . . . . . . . . . . . . 26
Introduction of Machine Learning in Healthcare . . . . . . . . . . . . . . . . . . . . 27
Introduction to Vector Machine Techniques . . . . . . . . . . . . . . . . . . . . . . . . 28
Introduction to OCR Techniques in Healthcare . . . . . . . . . . . . . . . . . . . . . 30
Flood of Paper Claims After the Arrival of Optical
Character Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Electronic Exchange of Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Advantages of OCR in Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Privacy and Security in Smart Health Records . . . . . . . . . . . . . . . . . . . . . . 32
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Automation of COVID-19 Disease Diagnosis from Radiograph . . . . . . . . 37
Keerthi Mangond, B. S. Divya, N. Siva Rama Lingham,
and Thompson Stephan
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Proposed Classification Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
First Phase (Data Augmentation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Second Phase Pre-Trained VGG16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
COVID-19 Classification Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Confusion Matrix and Feature Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Comparison with State-of-the-Art Methods . . . . . . . . . . . . . . . . . . . . . . . . 44
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Applications of Artificial Intelligence in the Attainment
of Sustainable Development Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Nisha Solanki, Archana Chaudhary, and Dinesh Bhatia
Introduction to Artificial Intelligence and Sustainable
Developmental Goals (SDGs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Artificial Intelligence and SDGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51