Long description:

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.

• 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

Table of Contents:

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