Data science in the medical field /

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 le...

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Bibliographic Details
Main Authors:	Kadry, Seifedine, 1977- (Author), Mahajan, Shubham (Author)
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	London, United Kingdom : Academic Press, [2025]
Subjects:	Medical informatics. Medical statistics. Médecine > Informatique. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter 1. PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata
Chapter 2. An automatic detection and severity levels of COVID-19 using convolutional neural network models
Chapter 3. Biosensors and disease diagnostics in medical field
Chapter 4. Brain tumor recognition and classification techniques
Chapter 5. Identifying the features and attributes of various artificial intelligence-based healthcare models
Chapter 6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
Chapter 7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
Chapter 8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care
Chapter 9. Data science in medical field: advantages, challenges, and opportunities
Chapter 10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
Chapter 11. Machine learning in heart disease prediction
Chapter 12. U-Net-based approaches for brain tumor segmentation
Chapter 13. Explainable image recognition models for aiding radiologists in clinical decision making
Chapter 14. Prediction of heart failure disease using classification algorithms along with performance parameters
Chapter 15. Cancer survival prediction using artificial intelligence: current status and future prospects
Chapter 16. Heart disease prediction in pregnant women with diabetes using machine learning
Chapter 17. Healthcare using image recognition technology
Chapter 18. Integration of deep learning and blockchain technology for a smart healthcare record management system
Chapter 19. Internet of things based smart health and attendance monitoring system in an institution for COVID-19
Chapter 20. Medical diagnosis using image processing techniques
Chapter 21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
Chapter 22. Predictive analysis in healthcare using data science
Chapter 23. Recommender systems in healthcare--an emerging technology
Chapter 24. Robotics: challenges and opportunities in healthcare
Chapter 25. A new era of the healthcare industry using Internet of Medical Things
Chapter 26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
Chapter 27. Analyzing the success of the thriving machine prediction model for Parkinson's disease prognosis: a comprehensive review.