Internet of Things and Machine Learning for Type I and Type II Diabetes

English | 2024 | PDF | 41 MB | 410 Pages

Sujata Dash, Subhendu Kumar Pani, Willy Susilo, Cheung Man Yung Bernard, Gary Tse, B0D93QPZ8D, 0323956866, 0323956939, 9780323956932, 978-0323956932, 9780323956864, 978-0323956864, 978-0-323-95686-4

Internet of Things and Machine Learning for Type I and Type II Diabetes: Use Cases provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.

– Integrates many Machine learning techniques in biomedical domain to detect various types of diabetes to utilizing large volumes of available diabetes-related data for extracting knowledge
– It integrates data mining and IoT techniques to monitor diabetes patients using their medical records (HER) and administrative data
– Includes clinical applications to highlight contemporary use of these machine learning algorithms and artificial intelligence-driven models beyond research settings