Disease Prediction using Machine Learning, Deep Learning and Data Analytics

✍ Scribed by Geeta Rani, Vijaypal Singh Dhaka (editor), Pradeep Kumar Tiwari (editor)

• Publisher: Bentham Science Publishers
• Year: 2024
• Tongue: English
• Leaves: 194
• Category: Library

✦ Synopsis

This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes.

The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases.

Key topics:

- Federated learning in analysis of sensitive healthcare data while preserving privacy and security.

- Artificial intelligence for 3-D bone image reconstruction.

- Detection of disease severity and creating personalized treatment plans using machine learning and software tools

- Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases

- Brain-computer interfaces

- Data mining for standardized electronic health records

- Data collection, management, and analysis in epidemiological research

The book is a resource for learners and professionals in healthcare service training programs and health administration departments.

✦ Table of Contents

Cover
Title
Copyright
End User License Agreement
Contents
Foreword
Preface
Introduction
Dedication
List of Contributors
Role of Federated Learning in Healthcare: A Review
Geeta Rani3, Meet Oza1, Heta Patel1, Vijaypal Singh Dhaka3, and Sushma Hans2
INTRODUCTION
LITERATURE REVIEW
METHODOLOGY
EXPERIMENTS
VGG-16 [30]
AlexNet [31]
ResNet101 [32]
DenseNet121 [33]
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Role of Artificial Intelligence in 3-D Bone Image Reconstruction: A Review
Nitesh Pradhan3, Vijaypal Singh Dhaka1, Geeta Rani1, and Monika Agarwal2
INTRODUCTION
ANALYSIS OF RELATED WORK
CONCLUSION
REFERENCES
Role of Machine Learning and Deep Learning Techniques in Detection of Disease Severity: A Survey
Geeta Rani1, Vijaypal Singh Dhaka1 and Sushma Hans2
INTRODUCTION
LITERATURE REVIEW
Severity Detection using Machine Learning
Severity Detection using Deep Learning
CONCLUSION
REFERENCES
Computer-aided Bio-medical Tools for Disease Identification
E. Francy Irudaya Rani1, T. Lurthu Pushparaj2 and E. Fantin Irudaya Raj3,
INTRODUCTION
APPLICATIONS OF CAD IN MEDICAL ANALYSIS
Cardiology Study using CAD
Ophthalmology Study using CAD
Dermatology Study using CAD
Pathology Study using CAD
IMAGE PROCESSING METHODOLOGY ADOPTED IN CAD
Pre-processing
Active Contour Method
Seeded Region Growing Method
Morphological Operations
SEGMENTATION
Edge Detection for Segmentation
Thresholding Method for Segmentation
Region-Based Methods for Segmentation
Clustering Based Methods for Segmentation
Hybrid Image Segmentation using Watershed and Fast Region Merging
FEATURE SELECTION
Feature Selection in Brain Imaging
Feature Selection in Alzheimer’s Disease
Feature Selection in Lung Disease
Feature Selection in Eye Disease
FEATURE SELECTION FOR CLASSIFICATION
CLASSIFICATION
Statistical Classification Methods
Rule-Based Systems Classification
Neural Network Classifiers
SUPPORT VECTOR MACHINE (SVM) FOR CLASSIFICATION
DISCUSSION OF CAD TOOLS FOR MEDICAL APPLICATION
CONCLUSION
REFERENCES
Prognosis of Dementia using Machine Learning
Anu Saini1, Sunita Kumari1,, Ritik 1, Rajni 1 and Sushma Hans2
INTRODUCTION
RELATED WORK
METHODOLOGY
Proposed Model for Predicting Dementia using Patient Record and MRI
RESULT ANALYSIS
CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
A Clinical Decision Support System for Effective Identification of the Onset of Asthma Disease
M.R. Pooja1,
INTRODUCTION
RELATED WORK
MATERIAL AND METHODS
Dataset Description
Combatting Class Imbalance
Feature Clustering
Subject Clustering
Performance Evaluation
CONCLUSION
REFERENCES
Applying Deep Learning and Computer Vision for
Early Diagnosis of Eye Diseases
The Fusion of Human-Computer Interaction and Artificial Intelligence Leads to the Emergence of Brain Computer Interaction
M. Kiruthiga Devi1,
INTRODUCTION
COMPONENTS OF BRAIN COMPUTER INTERFACE
Signal Acquisition
Feature Extraction
Translation
Application/Device Output
BCI CHARACTERISTICS
BCI Systems are Classified according to how they use the Brain: Active BCI
Signal Acquisition Modalities have been used to Classify Structures as Invasive or Noninvasive BCI
Invasive Techniques
Non-Invasive Techniques
CHALLENGES
Training Process
Information Transfer Rate
Technical Challenges
Non-Linearity
Non-Stationary and Noise
Small Training Sets
CONCLUSION
REFERENCES
Mining Standardized EHR Data: Exploration, Issues, and Solution
Shivani Batra1,, Vinay Kumar1, Neha Kohli2 and Vaishali Arya2
INTRODUCTION
COMPLEXITY IN EHRS
IMPLEMENTING DM ON EHRS
CHALLENGES IN MINING STANDARDIZED EHRS
SOLUTION FOR MINING STANDARDIZED EHRS DATABASE
RELATED WORK
CONCLUSION
REFERENCES
Role of Database in Epidemiological Situation
Kanika Soni1, Shelly Sachdeva1 and Shivani Batra2,*
INTRODUCTION
Role of Data
Role of the Database
Epidemiology
JOURNEY OF DATABASES
EPIDEMIOLOGICAL SCENARIO AND DATABASES
IMPLEMENTATION DETAILS
Dataset Description
Query Scenarios
DATA ANALYSIS AND VISUALIZATION
FUTURE WORK
CONCLUSION
REFERENCES
Subject Index
Back Cover