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

<p>Section 1: Diagnosis<br>1. An Intelligent Diagnostic approach for diabetes Using rule-based Machine Learning techniques<br>2. Ensemble Sparse Intelligent Mining Techniques for Diabetes Diagnosis<br>3. Detection of Diabetic Retinopathy Using Neural Networks<br>4. An Intelligent Remote Diagnostic Approach for Diabetes Using Machine Learning Techniques<br>5. Diagnosis of Diabetic Retinopathy in Retinal Fundus Images Using Machine Learning and Deep Learning Models<br>6. Diagnosis of Diabetes Mellitus using Deep Learning Techniques and Big Data<br><br>Section 2: Glucose monitoring<br>7. IoT and Machine Learning for Management of Diabetes Mellitus<br>8. Prediction of glucose concentration in type 1 diabetes patients based on Machine learning techniques<br>9. ML-Based PCA Methods to Diagnose Statistical Distribution of Blood Glucose Levels of Diabetic Patients<br> <br>Section 3: Prediction of complications and risk stratification<br>10. Overview of New trends on deep learning models for diabetes risk prediction<br>11. Clinical applications of deep learning in diabetes and its enhancements with future predictions<br>12. Feature Classification and Extraction of Medical Data Related to Diabetes Using Machine Learning Techniques: A Review<br>13. ML-based predictive model for type 2 diabetes mellitus using genetic and clinical data<br>14. Applications of IoT and data mining techniques for diabetes monitoring<br>15. Decision-making System for the Prediction of Type II Diabetes Using Data Balancing and Machine Learning Techniques<br>16. Comparative Analysis of Machine Learning Tools in Diabetes Prediction<br>17. Data Analytic models of patients dependent on insulin treatment<br>18. Prediction of Diabetes using Hybridization of Radial Basis Function Network and Differential Evaluation based Optimization Technique<br>19. An Overview of New Trends On Deep Learning Models For Diabetes Risk Prediction<br> <br>Section 4: Dialysis<br>20. Progression and Identification of heart disease risk factors in diabetic patients from electronic health records<br>21. An Intelligent Fog Computing-based Diabetes Prediction System for Remote Healthcare Applications<br>22. Artificial intelligence approaches for risk stratification of diabetic kidney disease<br>23. Computational Methods for predicting the occurrence of cardiac autonomic neuropathy<br>24. Development of a Clinical Forecasting Model to Predict Comorbid Depression in Diabetes Patients and its Application in Policy Making for Depression Screening<br><br>Section 5: Drug design and Treatment Response<br>25. Enhancing Diabetic Maculopathy Classification through a Synergistic Deep Learning Approach by Combining Convolutional Neural Networks, Transfer Learning, and Attention Mechanisms<br>26. Pharmacogenomics: the roles of genetic factors on treatment response and outcomes in diabetes<br>27. Predicting treatment response in diabetes: the roles of machine learning-based models<br>28. Antidiabetic Potential of Mangrove Plants: An Updated Review</p>