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Disease Prediction using
Machine Learning, Deep
Learning and Data Analytics
Edited by
Geeta Rani
Department of Computer and Communication Engineering
Manipal University Jaipur
Jaipur, India
Vijaypal Singh Dhaka
Department of Computer and Communication Engineering
Manipal University Jaipur
Jaipur, India
&
Pradeep Kumar Tiwari
Dr. Vishwanath Karad MIT
World Peace University
Pune, India
Disease Prediction using Machine Learning, Deep Learning and Data Analytics
Editors: Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari
ISBN (Online): 978-981-5179-12-5
ISBN (Print): 978-981-5179-13-2
ISBN (Paperback): 978-981-5179-14-9
© 2024, Bentham Books imprint.
Published by Bentham Science Publishers Pte. Ltd. Singapore. All Rights Reserved.
First published in 2024.
BSP-EB-EBH-9789815179125-TP-177-TC-10-PD-20240307
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CONTENTS
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FOREWORD ........................................................................................................................................... i
PREFACE ................................................................................................................................................ ii
INTRODUCTION ................................................................................................................................... vi
DEDICATION ......................................................................................................................................... viii
LIST OF CONTRIBUTORS .................................................................................................................. ix
CHAPTER 1 ROLE OF FEDERATED LEARNING IN HEALTHCARE: A REVIEW .............. 1
Geeta Rani, Meet Oza, Heta Patel, Vijaypal Singh Dhaka and Sushma Hans
INTRODUCTION .......................................................................................................................... 2
LITERATURE REVIEW .............................................................................................................. 4
METHODOLOGY ......................................................................................................................... 8
EXPERIMENTS ............................................................................................................................. 10
VGG-16 [30] ........................................................................................................................... 11
AlexNet [31] ........................................................................................................................... 11
ResNet101 [32] ....................................................................................................................... 12
DenseNet121 [33] ................................................................................................................... 12
RESULTS AND DISCUSSION ..................................................................................................... 13
CONCLUSION ............................................................................................................................... 14
REFERENCES ............................................................................................................................... 15
CHAPTER 2 ROLE OF ARTIFICIAL INTELLIGENCE IN 3-D BONE IMAGE
RECONSTRUCTION: A REVIEW ...................................................................................................... 17
Nitesh Pradhan, Vijaypal Singh Dhaka, Geeta Rani and Monika Agarwal
INTRODUCTION .......................................................................................................................... 17
ANALYSIS OF RELATED WORK ............................................................................................. 19
CONCLUSION ............................................................................................................................... 26
REFERENCES ............................................................................................................................... 27
CHAPTER 3 ROLE OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES IN
DETECTION OF DISEASE SEVERITY: A SURVEY ...................................................................... 31
Geeta Rani, Vijaypal Singh Dhaka and Sushma Hans
INTRODUCTION .......................................................................................................................... 32
LITERATURE REVIEW .............................................................................................................. 37
Severity Detection using Machine Learning ........................................................................... 37
Severity Detection using Deep Learning ................................................................................ 41
CONCLUSION ............................................................................................................................... 48
REFERENCES ............................................................................................................................... 49
CHAPTER 4 COMPUTER-AIDED BIO-MEDICAL TOOLS FOR DISEASE
IDENTIFICATION ................................................................................................................................. 52
E. Francy Irudaya Rani, T. Lurthu Pushparaj and E. Fantin Irudaya Raj
INTRODUCTION .......................................................................................................................... 53
APPLICATIONS OF CAD IN MEDICAL ANALYSIS ............................................................. 53
Cardiology Study using CAD ................................................................................................. 54
Ophthalmology Study using CAD .......................................................................................... 55
Dermatology Study using CAD .............................................................................................. 56
Pathology Study using CAD ................................................................................................... 57
IMAGE PROCESSING METHODOLOGY ADOPTED IN CAD ............................................ 58
Pre-processing ......................................................................................................................... 58
Active Contour Method .......................................................................................................... 59
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Seeded Region Growing Method ............................................................................................ 59
Morphological Operations ...................................................................................................... 60
SEGMENTATION ......................................................................................................................... 61
Edge Detection for Segmentation ........................................................................................... 62
Thresholding Method for Segmentation ................................................................................. 62
Region-Based Methods for Segmentation .............................................................................. 63
Clustering Based Methods for Segmentation ......................................................................... 63
Hybrid Image Segmentation using Watershed and Fast Region Merging .............................. 64
FEATURE SELECTION ............................................................................................................... 65
Feature Selection in Brain Imaging ........................................................................................ 67
Feature Selection in Alzheimer’s Disease .............................................................................. 67
Feature Selection in Lung Disease .......................................................................................... 67
Feature Selection in Eye Disease ............................................................................................ 68
FEATURE SELECTION FOR CLASSIFICATION .................................................................. 68
CLASSIFICATION ........................................................................................................................ 70
Statistical Classification Methods ........................................................................................... 70
Rule-Based Systems Classification ......................................................................................... 70
Neural Network Classifiers ..................................................................................................... 70
SUPPORT VECTOR MACHINE (SVM) FOR CLASSIFICATION ....................................... 70
DISCUSSION OF CAD TOOLS FOR MEDICAL APPLICATION ........................................ 71
CONCLUSION ............................................................................................................................... 73
REFERENCES ............................................................................................................................... 74
CHAPTER 5 PROGNOSIS OF DEMENTIA USING MACHINE LEARNING ........................... 80
Anu Saini, Sunita Kumari, Ritik, Rajni and Sushma Hans
INTRODUCTION .......................................................................................................................... 80
RELATED WORK ......................................................................................................................... 82
METHODOLOGY ......................................................................................................................... 85
Proposed Model for Predicting Dementia using Patient Record and MRI ............................. 85
RESULT ANALYSIS ..................................................................................................................... 87
CONCLUSION ............................................................................................................................... 89
ACKNOWLEDGMENTS .............................................................................................................. 90
REFERENCES ............................................................................................................................... 90
CHAPTER 6 A CLINICAL DECISION SUPPORT SYSTEM FOR EFFECTIVE
IDENTIFICATION OF THE ONSET OF ASTHMA DISEASE ....................................................... 92
M.R. Pooja
INTRODUCTION .......................................................................................................................... 92
RELATED WORK ......................................................................................................................... 93
MATERIAL AND METHODS ..................................................................................................... 94
Dataset Description ................................................................................................................. 94
Combatting Class Imbalance .................................................................................................. 94
Feature Clustering ................................................................................................................... 94
Subject Clustering ................................................................................................................... 95
Performance Evaluation .......................................................................................................... 97
CONCLUSION ............................................................................................................................... 100
REFERENCES ............................................................................................................................... 100
CHAPTER 7 APPLYING DEEP LEARNING AND COMPUTER VISION FOR EARLY
DIAGNOSIS OF EYE DISEASES ......................................................................................................... 103
Shradha Dubey and Manish Dixit
INTRODUCTION .......................................................................................................................... 104
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MOTIVATION ............................................................................................................................... 104
TECHNICAL ASPECTS OF DEEP LEARNING ...................................................................... 105
Benefits of Deep Learning ...................................................................................................... 106
LITERATURE REVIEW .............................................................................................................. 107
IMAGING MODALITIES ............................................................................................................ 109
Ultrasound Imaging ................................................................................................................ 109
Advantages .................................................................................................................... 110
Disadvantages ............................................................................................................... 110
Optical Coherence Tomography (OCT) ................................................................................. 110
Advantages .................................................................................................................... 111
Disadvantages ............................................................................................................... 111
Color Fundus Photography ..................................................................................................... 111
Advantages .................................................................................................................... 113
Disadvantages ............................................................................................................... 113
Fundus Fluorescein Angiography (FFA) ............................................................................... 114
Advantages .................................................................................................................... 114
Disadvantages ............................................................................................................... 114
Heidelberg Retinal Tomography (HRT) ................................................................................ 114
Advantages .................................................................................................................... 115
Disadvantages ............................................................................................................... 115
Slit-Lamp Photography .......................................................................................................... 115
Advantages .................................................................................................................... 115
Disadvantages ............................................................................................................... 116
EYE DISEASES .............................................................................................................................. 117
Diabetic Retinopathy ............................................................................................................. 118
Age-Related Macular Degeneration ...................................................................................... 119
Diabetic Macular Edema ........................................................................................................ 120
Glaucoma ............................................................................................................................... 120
Cataract .................................................................................................................................. 122
RESEARCH CHALLENGES ....................................................................................................... 126
CONCLUSION ............................................................................................................................... 126
REFERENCES ............................................................................................................................... 127
CHAPTER 8 THE FUSION OF HUMAN-COMPUTER INTERACTION AND ARTIFICIAL
INTELLIGENCE LEADS TO THE EMERGENCE OF BRAIN COMPUTER INTERACTION 131
M. Kiruthiga Devi
INTRODUCTION .......................................................................................................................... 132
COMPONENTS OF BRAIN COMPUTER INTERFACE ........................................................ 133
Signal Acquisition ................................................................................................................... 133
Feature Extraction ................................................................................................................... 134
Translation .............................................................................................................................. 134
Application/Device Output ..................................................................................................... 134
BCI CHARACTERISTICS ........................................................................................................... 134
BCI Systems are Classified according to how they use the Brain: Active BCI ..................... 134
Signal Acquisition Modalities have been used to Classify Structures as Invasive or
Noninvasive BCI ..................................................................................................................... 135
Invasive Techniques ...................................................................................................... 135
Non-Invasive Techniques .............................................................................................. 136
CHALLENGES ............................................................................................................................... 142
Training Process ...................................................................................................................... 142
Information Transfer Rate ....................................................................................................... 142
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Technical Challenges .............................................................................................................. 142
Non-Linearity .......................................................................................................................... 142
Non-Stationary and Noise ....................................................................................................... 142
Small Training Sets ................................................................................................................. 143
CONCLUSION ............................................................................................................................... 143
REFERENCES ............................................................................................................................... 143
CHAPTER 9 MINING STANDARDIZED EHR DATA: EXPLORATION, ISSUES, AND
SOLUTION .............................................................................................................................................. 146
Shivani Batra, Vinay Kumar, Neha Kohli and Vaishali Arya
INTRODUCTION .......................................................................................................................... 146
COMPLEXITY IN EHRS ............................................................................................................. 147
IMPLEMENTING DM ON EHRS ............................................................................................... 148
CHALLENGES IN MINING STANDARDIZED EHRS ........................................................... 151
SOLUTION FOR MINING STANDARDIZED EHRS DATABASE ....................................... 152
RELATED WORK ......................................................................................................................... 154
CONCLUSION ............................................................................................................................... 156
REFERENCES ............................................................................................................................... 156
CHAPTER 10 ROLE OF DATABASE IN EPIDEMIOLOGICAL SITUATION ......................... 159
Kanika Soni, Shelly Sachdeva and Shivani Batra
INTRODUCTION .......................................................................................................................... 159
Role of Data ............................................................................................................................ 160
Role of the Database ............................................................................................................... 161
Epidemiology .......................................................................................................................... 161
JOURNEY OF DATABASES ....................................................................................................... 162
EPIDEMIOLOGICAL SCENARIO AND DATABASES .......................................................... 165
IMPLEMENTATION DETAILS .................................................................................................. 166
Dataset Description ................................................................................................................. 166
Query Scenarios ...................................................................................................................... 167
DATA ANALYSIS AND VISUALIZATION ............................................................................... 167
FUTURE WORK ............................................................................................................................ 170
CONCLUSION ............................................................................................................................... 170
REFERENCES ............................................................................................................................... 171
SUBJECT INDEX
....................................................................................................................................
172
FOREWORD
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It is my pleasure to write the foreword for the book titled “Disease Prediction using
Machine Learning, Deep Learning and Data Analytics”. The book covers the role of
machine learning in boosting the immunity of a person, role of federated learning in
healthcare, role of data mining in developing a medical support system, and role of AI in
establishing interaction between human brain and computer.
This book covers the detection of diabetic retinopathy using machine learning algorithms,
deep learning based model for conversion of 2-D images into to 3-D images, developing a
decision support system for prediction of asthma attack, early prediction of eye diseases,
computer-aided bio-medical tools for disease identification, deep learning based systems for
medical data classification and AI-based chatbot system for healthcare industry.
The book “Disease Prediction using Machine Learning, Deep Learning and Data Analytics”,
gives a clear idea about the deep learning techniques employed for analysis and classification
of imagery data. The data mining algorithms for knowledge extraction and feature extraction
techniques attract the readers working in the field of medical image analysis. The book
provides the mechanisms involved in designing and developing the clinical decision support
systems.
“Disease Prediction using Machine Learning, Deep Learning and Data Analytics”, is a must
read book for the academicians, researchers and students working in the field of applications
of machine learning and deep learning for disease diagnosis and prognosis. The book is
important to read for the clinical experts who are keen to adopt the techno-tools as assistants
for diagnosis and prognosis of diseases.
i
I would like to congratulate the Editor in Chief, Dr. Geeta Rani and Associate Editors, Dr.
Pradeep Kumar Tiwari and Dr. Vijaypal Singh Dhaka for bringing the ideas of academicians
and research community together at a single platform. I strongly believe that their expertise in
the field of machine learning, cloud computing and medical image analysis will be effective
in attracting the readers in the field.
Dharm Singh Jat
Namibia University of Science & Technology
Windhoek, Namibia
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