Data Science in the Medical Field

1st Edition - September 25, 2024
Authors: Seifedine Kadry, Shubham Mahajan
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 4 0 2 8 - 7
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 4 0 2 9 - 4

Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of da… Read more

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Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

Cover image
Title page
Table of Contents
Copyright
List of contributors
About the authors
Preface
Chapter 1. PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata
Abstract
1.1 Introduction
1.2 A brief survey of related technologies and past important works
1.3 Research methodology and proposed framework
1.4 Discussions
1.5 Conclusions and future research direction
Acknowledgment
Declaration of competing interest
References
Chapter 2. An automatic detection and severity levels of COVID-19 using convolutional neural network models
Abstract
2.1 Introduction
2.2 Related work
2.3 Early diagnosis by deep learning strategies
2.4 Methodology
2.5 Dataset and implementation
2.6 Performance evaluation metrics
2.7 Comparison
2.8 Conclusion
References
Chapter 3. Biosensors and disease diagnostics in medical field
Abstract
3.1 Introduction
3.2 Principle of biosensor
3.3 Architecture of biosensor structure
3.4 Different types of medical sensors
3.5 Biosensor technologies in disease diagnosis
3.6 Application of biosensors
3.7 Challenges and limitations of biosensors in disease diagnostics
3.8 Future perspectives and advancements in biosensor technology
3.9 Commercial and clinical adoption of biosensors
3.10 Case studies of biosensor applications
3.11 Conclusion
References
Chapter 4. Brain tumor recognition and classification techniques
Abstract
4.1 Introduction
4.2 Imaging methods
4.3 Brain tumor detection, segmentation, and classification
4.4 Conclusion and discussion for segmentation
4.5 Analysis study
4.6 Conclusion and discussion for classification
4.7 Conclusion and future prospects
References
Chapter 5. Identifying the features and attributes of various artificial intelligence-based healthcare models
Abstract
5.1 Introduction
5.2 Predictive analytics models
5.3 Natural Language Processing models
5.4 Chatbot models
5.5 Computer Vision models
5.6 Conclusion
5.7 Artificial intelligence disclosure
References
Chapter 6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
Abstract
6.1 Introduction
6.2 Related works
6.3 Types of classification
6.4 Architecture and methods
6.5 Proposed attributes and methods
6.6 Efficiency and performance
6.7 Experiential result discussion
6.8 Conclusion
References
Chapter 7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
Abstract
7.1 Introduction
7.2 Related works
7.3 Methodology
7.4 Experimental analysis
7.5 Conclusion
Author contribution
Acknowledgment
References
Chapter 8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care
Abstract
8.1 Introduction
8.2 Data science in healthcare: an overview
8.3 Predictive analysis techniques in healthcare
8.4 Application of predictive analysis in healthcare
8.5 Case studies and success stories
8.6 Conclusion and future research direction
References
Chapter 9. Data science in medical field: advantages, challenges, and opportunities
Abstract
9.1 Introduction
9.2 Literature review
9.3 Overview of data science in medical field
9.4 Applications of data science in medical field
9.5 Advantages of data science in healthcare sector
9.6 Challenges of data science in healthcare sector
9.7 Opportunities of data science in healthcare sector
9.8 Discussion and future directions
9.9 Conclusion
Further reading
Chapter 10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
Abstract
10.1 Introduction
10.2 Literature review
10.3 Network architecture and implementation
10.4 Application development and smart-contract deployment
10.5 Results and discussion
10.6 Conclusion
10.7 Future work
References
Chapter 11. Machine learning in heart disease prediction
Abstract
11.1 Introduction
11.2 Literature review
11.3 Proposed method
11.4 Methodology
11.5 Software requirement
11.6 Conclusion
References
Chapter 12. U-Net-based approaches for brain tumor segmentation
Abstract
12.1 Introduction
12.2 Brain tumors
12.3 Magnetic resonance imaging
12.4 Deep learning
12.5 Convolutional neural networks
12.6 U-Net
12.7 Summary of related work
12.8 Experimental setup
12.9 Model building and training
12.10 2D U-Net architecture
12.11 2D Modalities results
12.12 3D U-Net architecture
12.13 3D modalities results
12.14 Residual U-Net architecture
12.15 Activation function results
12.16 Normalization and dropout results
12.17 Attention U-Net architecture
12.18 Normalization and dropout results
12.19 Residual attention U-Net architecture
12.20 Normalization and dropout results
12.21 Architecture comparison
12.22 Conclusion
12.23 Research contribution
12.24 Future work
References
Chapter 13. Explainable image recognition models for aiding radiologists in clinical decision making
Abstract
13.1 Introduction
13.2 Literature review
13.3 Proposed work
13.4 X-ray
13.5 Magnetic resonance imaging scan
13.6 Experimental results
13.7 Concluding remarks and prospects
References
Chapter 14. Prediction of heart failure disease using classification algorithms along with performance parameters
Abstract
14.1 Introduction
14.2 Related work
14.3 Methodology
14.4 Conclusion
References
Chapter 15. Cancer survival prediction using artificial intelligence: current status and future prospects
Abstract
15.1 Introduction
15.2 Literature review
15.3 Evaluation metrics for cancer survival prediction
15.4 Challenges and limitations of using artificial intelligence techniques
15.5 Conclusion and future direction
References
Chapter 16. Heart disease prediction in pregnant women with diabetes using machine learning
Abstract
16.1 Introduction
16.2 Literature review
16.3 Proposed research work
16.4 Results and discussion
16.5 Performance metrics for machine learning models: logistic regression, random forest, and decision tree
16.6 Conclusion
16.7 Future scope
AI disclosure
References
Chapter 17. Healthcare using image recognition technology
Abstract
17.1 Introduction
17.2 What is machine learning and how does it work?
17.3 Master’s in healthcare
17.4 Discussion on medical image processing
17.5 Conclusion
References
Chapter 18. Integration of deep learning and blockchain technology for a smart healthcare record management system
Abstract
18.1 Introduction
18.2 Importance of smart healthcare
18.3 Emerging technologies in Internet of Medical Things
18.4 Digital twins, telemedicine, and metaverse in Internet of Medical Things
18.5 Case study: patient centric healthcare model
18.6 Results
18.7 Discussion
18.8 Conclusion
References
Chapter 19. Internet of things based smart health and attendance monitoring system in an institution for COVID-19
Abstract
19.1 Introduction
19.2 Coronavirus
19.3 Different technologies
19.4 Result and discussion
19.5 Conclusions
References
Chapter 20. Medical diagnosis using image processing techniques
Abstract
20.1 Introduction
20.2 Image processing techniques in medical diagnosis
20.3 Recent advancements in medical diagnosis
20.4 Methodology and applications
20.5 Advancements in image processing
20.6 Challenges in medical diagnosis using image processing
20.7 Potential solutions and future directions
20.8 Evaluation metrics and performance analysis
20.9 Conclusion
References
Chapter 21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
Abstract
21.1 Introduction
21.2 Healthcare predictive modeling
21.3 The use of machine learning in the medical business
21.4 Healthcare predictive analytics example
21.5 The use of predictive analytics with the use of machine learning
21.6 Conclusion
References
Chapter 22. Predictive analysis in healthcare using data science
Abstract
22.1 Introduction
22.2 Related works
22.3 An in-depth look of data science
22.4 Data science in the world of healthcare
22.5 The healthcare sector
22.6 Strategies and tools for using data science in healthcare
22.7 A guide to data science in healthcare: applications
22.8 Data science’s effects on healthcare
22.9 Healthcare benefits of data science
22.10 Challenges
22.11 Healthcare data science future
22.12 Conclusion
References
Chapter 23. Recommender systems in healthcare—an emerging technology
Abstract
23.1 Introduction
23.2 Recommender Systems in Hhealthcare
23.3 Major challenges in Rrecommender Ssystems
23.4 Conclusion
References
Chapter 24. Robotics: challenges and opportunities in healthcare
Abstract
24.1 Introduction
24.2 Research method
24.3 Literature survey
24.4 Advantages of robot in healthcare sectors
24.5 Applications of robotics in healthcare
24.6 Challenges in implementing robotics in healthcare
24.7 Conclusion
References
Chapter 25. A new era of the healthcare industry using Internet of Medical Things
Abstract
25.1 Introduction
25.2 Structure of Internet of Things-based healthcare system
25.3 Literature review
25.4 Types of Internet of Medical Things devices
25.5 Components of Internet of Medical Things
25.6 Benefits of Internet of Medical Things
25.7 Challenges of Internet of Medical Things
25.8 Conclusion and future work
References
Chapter 26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
Abstract
26.1 Introduction
26.2 Advancement of machine learning classifiers for the study of endometriosis
26.3 Single-cell analysis of endometriosis
26.4 Gene expression analysis of endometrium
26.5 Identification of novel drug targets for endometriosis
26.6 Discussion
Acknowledgments
References
Chapter 27. Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review
Abstract
27.1 Introduction
27.2 Related works
27.3 Methods
27.4 Discussions
27.5 Conclusion
References
Author Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Subject Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Z

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Data Science in the Medical Field

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Seifedine Kadry

Shubham Mahajan

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