Data science has the potential to influence and improve fundamental services like the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 terabytes of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, like 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 illness signs at an extremely early stage. Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applicationsThe book combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologiesProvides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book INDICE: 1. PPH 4.0: A privacy-preserving Health 4.0 framework with ML and CA 2. An Automatic Detection and Severity Levels of Covid-19 Using CNN Models 3. Biosensors and Disease Diagnostics in Medical Field 4. Brain Tumor Recognition and Classification Techniques 5. Identifying the Features and Attributes of various AI-based Healthcare Models 6. Classification in Medical Applications Data Set Using Optimization Techniques 7. A Knowledge Discovery framework for Coronavirus Disease 2019 (COVID -19) disease from PubMed Abstract using Association Rule Hypergraph (AR-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 IoMT Data Through Smart Contracts in Telecare Medical Information Systems 11. Machine Learning in Heart Disease Prediction 12. Au-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. Health Care Using Image Recognition Technology 18. Integration of Deep Learning and Blockchain Technology for a Smart Healthcare Record Management System 19. IoT-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 Health Care- An Emerging Technology 24. Robotics: Challenges and Opportunities in Healthcare 25. A new era of the healthcare industry using IoT: Internet of Medical Things (IoMT) 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 (TMPM) for Parkinson’s Disease Prognosis: A Comprehensive Review
- ISBN: 978-0-443-24028-7
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 255
- Fecha Publicación: 01/10/2024
- Nº Volúmenes: 1
- Idioma: Inglés