
Data Science in the Medical Field
- 1st Edition - September 25, 2024
- Imprint: Academic Press
- 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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Request a sales quoteData 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.
- Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications
- Combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologies
- Provides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book
Researchers working in the healthcare or medical sectors- clinicians, academics and in industrial R&D. Final year undergraduate and postgraduate students
- 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
- Edition: 1
- Published: September 25, 2024
- Imprint: Academic Press
- No. of pages: 458
- Language: English
- Paperback ISBN: 9780443240287
- eBook ISBN: 9780443240294
SK
Seifedine Kadry
Seifedine Kadry is a Professor in the Department of Mathematics and Computer Science, at Norrof University College, in Norway. He has a Bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present, his research focuses on data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.
SM
Shubham Mahajan
Dr. Shubham Mahajan is a distinguished academic and professional member of prestigious organizations such as IEEE, ACM, and IAENG. He earned his B.Tech. from Baba Ghulam Shah Badshah University, his M.Tech. from Chandigarh University, his Ph.D. from Shri Mata Vaishno Devi University in India, and his Postdoctoral degree in Applied Data Science at Noroff University College in Norway. Currently, he is working as an Assistant Professor at Amity University, Haryana, India.
Dr. Mahajan specializes in artificial intelligence, image processing and segmentation, data mining, and machine learning, holding eleven Indian patents along with one patent each from Australia and Germany.