Internet of Things and Machine Learning for Type I and Type II Diabetes
Use Cases
Herausgeber: Dash, Sujata; Tse, Gary; Yung Bernard, Cheung Man; Susilo, Willy; Pani, Subhendu Kumar
Internet of Things and Machine Learning for Type I and Type II Diabetes
Use Cases
Herausgeber: Dash, Sujata; Tse, Gary; Yung Bernard, Cheung Man; Susilo, Willy; Pani, Subhendu Kumar
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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.…mehr
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- Produktdetails
- Verlag: Elsevier Science
- Seitenzahl: 448
- Erscheinungstermin: 9. Juli 2024
- Englisch
- Gewicht: 450g
- ISBN-13: 9780323956864
- ISBN-10: 0323956866
- Artikelnr.: 69769461
- Verlag: Elsevier Science
- Seitenzahl: 448
- Erscheinungstermin: 9. Juli 2024
- Englisch
- Gewicht: 450g
- ISBN-13: 9780323956864
- ISBN-10: 0323956866
- Artikelnr.: 69769461
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
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