Internet of Things and Machine Learning for Type I and Type II Diabetes: Use Cases

ISBN-13: 9780323956864 / Angielski

Sujata Dash; Subhendu Kumar Pani; Willy Susilo

Spis treści

Section I: Diagnosis 1. An Intelligent Diagnostic approach for diabetes Using rule-based Machine Learning techniques 2. Ensemble sparse intelligent mining techniques Section II: Glucose monitoring 3. Glucose monitoring system using IoT 4. Prediction of glucose concentration in type 1 diabetes patients based on Machine learning techniques 5. Prediction of Hypoglycaemia using machine learning models for patients with type 2 diabetes Section III: Prediction of complications and risk stratification 6. Overview of New trends on deep learning models for diabetes risk prediction 7. Clinical applications of deep learning in diabetes and its enhancements with future predictions 8. Feature Classification and Extraction of Medical Data related to Diabetes Using Machine Learning Techniques 9. ML-based predictive model for type 2 diabetes mellitus using genetic and clinical data 10. Applications of IoT and data mining techniques in monitoring diabetes care 11. Prevention and diagnosis of diabetes diseases using machine learning models. 12. Data Analytics models for patients dependent on insulin treatment 13. Progression and Identification of heart disease risk factors in diabetic patients from electronic health records 14. Classification of diabetes maculopathy images using machine learning techniques 15. Artificial intelligence-Based risk pattern prediction in diabetic kidney disease 16. Computational Methods for predicting the occurrence of cardiac autonomic neuropathy 17. Clinical forecasting of machine learning and IoT in comorbid depressions among diabetes patients Section IV: Drug design and Treatment Response 18. Machine learning-based drug design for diabetes mellitus 19. Pharmacogenomics: the roles of genetic factors on treatment response and outcomes in diabetes 20. Predicting treatment response in diabetes: the roles of machine learning-based models Section V: Summary 21. Clinical Applicability: perspectives from the physician 22. Current applications of ML algorithms for diabetes in the 23. healthcare systems: a UK perspective 24. Current applications of ML algorithms for diabetes in the 25. healthcare systems: a US perspective 26. Current applications of ML algorithms for diabetes in the 27. Healthcare systems: an Asian?(insert country) perspective 28. Patient experience of ML algorithms for diabetes