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 automata2. An automatic detection and severity levels of COVID-19 using convolutional neural network models3. Biosensors and disease diagnostics in medical field4. Brain tumor recognition and classification techniques5. Identifying the features and attributes of various artificial intelligence-based healthcare models6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care9. Data science in medical field: advantages, challenges, and opportunities10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems11. Machine learning in heart disease prediction12. U-Net-based approaches for brain tumor segmentation13. Explainable image recognition models for aiding radiologists in clinical decision making14. Prediction of heart failure disease using classification algorithms along with performance parameters15. Cancer survival prediction using artificial intelligence: current status and future prospects16. Heart disease prediction in pregnant women with diabetes using machine learning17. Healthcare using image recognition technology18. Integration of deep learning and blockchain technology for a smart healthcare record management system19. Internet of things based smart health and attendance monitoring system in an institution for COVID-1920. Medical diagnosis using image processing techniques21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care22. Predictive analysis in healthcare using data science23. Recommender systems in healthcare—an emerging technology24. Robotics: challenges and opportunities in healthcare25. A new era of the healthcare industry using Internet of Medical Things26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery27. Analyzing the success of the thriving machine prediction model for Parkinson's disease prognosis: a comprehensive review
