Internet of Things and Machine Learning for Type I and Type II Diabetes
-10%
portes grátis
Internet of Things and Machine Learning for Type I and Type II Diabetes
Use cases
Yung Bernard, Cheung Man; Susilo, Willy; Tse, Gary; Pani, Subhendu Kumar; Dash, Sujata
Elsevier - Health Sciences Division
07/2024
448
Mole
Inglês
9780323956864
15 a 20 dias
Descrição não disponível.
Section 1: Diagnosis 1. An Intelligent Diagnostic approach for diabetes Using rule-based Machine Learning techniques 2. Ensemble Sparse Intelligent Mining Techniques for Diabetes Diagnosis 3. Detection of Diabetic Retinopathy Using Neural Networks 4. An Intelligent Remote Diagnostic Approach for Diabetes Using Machine Learning Techniques 5. Diagnosis of Diabetic Retinopathy in Retinal Fundus Images Using Machine Learning and Deep Learning Models 6. Diagnosis of Diabetes Mellitus using Deep Learning Techniques and Big Data Section 2: Glucose monitoring 7. IoT and Machine Learning for Management of Diabetes Mellitus 8. Prediction of glucose concentration in type 1 diabetes patients based on Machine learning techniques 9. ML-Based PCA Methods to Diagnose Statistical Distribution of Blood Glucose Levels of Diabetic Patients Section 3: Prediction of complications and risk stratification 10. Overview of New trends on deep learning models for diabetes risk prediction 11. Clinical applications of deep learning in diabetes and its enhancements with future predictions 12. Feature Classification and Extraction of Medical Data Related to Diabetes Using Machine Learning Techniques: A Review 13. ML-based predictive model for type 2 diabetes mellitus using genetic and clinical data 14. Applications of IoT and data mining techniques for diabetes monitoring 15. Decision-making System for the Prediction of Type II Diabetes Using Data Balancing and Machine Learning Techniques 16. Comparative Analysis of Machine Learning Tools in Diabetes Prediction 17. Data Analytic models of patients dependent on insulin treatment 18. Prediction of Diabetes using Hybridization of Radial Basis Function Network and Differential Evaluation based Optimization Technique 19. An Overview of New Trends On Deep Learning Models For Diabetes Risk Prediction Section 4: Dialysis 20. Progression and Identification of heart disease risk factors in diabetic patients from electronic health records 21. An Intelligent Fog Computing-based Diabetes Prediction System for Remote Healthcare Applications 22. Artificial intelligence approaches for risk stratification of diabetic kidney disease 23. Computational Methods for predicting the occurrence of cardiac autonomic neuropathy 24. Development of a Clinical Forecasting Model to Predict Comorbid Depression in Diabetes Patients and its Application in Policy Making for Depression Screening Section 5: Drug design and Treatment Response 25. Enhancing Diabetic Maculopathy Classification through a Synergistic Deep Learning Approach by Combining Convolutional Neural Networks, Transfer Learning, and Attention Mechanisms 26. Pharmacogenomics: the roles of genetic factors on treatment response and outcomes in diabetes 27. Predicting treatment response in diabetes: the roles of machine learning-based models 28. Antidiabetic Potential of Mangrove Plants: An Updated Review
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<p>Diabetes; Mellitus; Diabetic Complications; disease prediction models; biomedical informatics; artificial intelligence; IoT; big data; machine learning</p>
Section 1: Diagnosis 1. An Intelligent Diagnostic approach for diabetes Using rule-based Machine Learning techniques 2. Ensemble Sparse Intelligent Mining Techniques for Diabetes Diagnosis 3. Detection of Diabetic Retinopathy Using Neural Networks 4. An Intelligent Remote Diagnostic Approach for Diabetes Using Machine Learning Techniques 5. Diagnosis of Diabetic Retinopathy in Retinal Fundus Images Using Machine Learning and Deep Learning Models 6. Diagnosis of Diabetes Mellitus using Deep Learning Techniques and Big Data Section 2: Glucose monitoring 7. IoT and Machine Learning for Management of Diabetes Mellitus 8. Prediction of glucose concentration in type 1 diabetes patients based on Machine learning techniques 9. ML-Based PCA Methods to Diagnose Statistical Distribution of Blood Glucose Levels of Diabetic Patients Section 3: Prediction of complications and risk stratification 10. Overview of New trends on deep learning models for diabetes risk prediction 11. Clinical applications of deep learning in diabetes and its enhancements with future predictions 12. Feature Classification and Extraction of Medical Data Related to Diabetes Using Machine Learning Techniques: A Review 13. ML-based predictive model for type 2 diabetes mellitus using genetic and clinical data 14. Applications of IoT and data mining techniques for diabetes monitoring 15. Decision-making System for the Prediction of Type II Diabetes Using Data Balancing and Machine Learning Techniques 16. Comparative Analysis of Machine Learning Tools in Diabetes Prediction 17. Data Analytic models of patients dependent on insulin treatment 18. Prediction of Diabetes using Hybridization of Radial Basis Function Network and Differential Evaluation based Optimization Technique 19. An Overview of New Trends On Deep Learning Models For Diabetes Risk Prediction Section 4: Dialysis 20. Progression and Identification of heart disease risk factors in diabetic patients from electronic health records 21. An Intelligent Fog Computing-based Diabetes Prediction System for Remote Healthcare Applications 22. Artificial intelligence approaches for risk stratification of diabetic kidney disease 23. Computational Methods for predicting the occurrence of cardiac autonomic neuropathy 24. Development of a Clinical Forecasting Model to Predict Comorbid Depression in Diabetes Patients and its Application in Policy Making for Depression Screening Section 5: Drug design and Treatment Response 25. Enhancing Diabetic Maculopathy Classification through a Synergistic Deep Learning Approach by Combining Convolutional Neural Networks, Transfer Learning, and Attention Mechanisms 26. Pharmacogenomics: the roles of genetic factors on treatment response and outcomes in diabetes 27. Predicting treatment response in diabetes: the roles of machine learning-based models 28. Antidiabetic Potential of Mangrove Plants: An Updated Review
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
