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

Use cases

1st Edition - July 7, 2024
Editors: Sujata Dash, Subhendu Kumar Pani, Willy Susilo, Cheung Man Yung Bernard, Gary Tse
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 6 8 6 - 4
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 6 9 3 - 2

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… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Section I. Diagnosis
Chapter 1. A systematic review on intelligent diagnosis of diabetes using rule-based machine learning techniques
1. Introduction
2. Literature search strategy
3. System overview
4. Dataset
5. Preprocessing methods
6. Algorithms for classification
7. Application scenarios
8. Limitations and future directions
9. Conclusions
Chapter 2. Ensemble sparse intelligent mining techniques for diabetes diagnosis
1. Introduction
2. Literature survey
3. Methodology
4. Result analysis
5. Conclusion and Future work
Chapter 3. Detection of diabetic retinopathy using Deep Neural networks
1. Introduction
2. System analysis
3. Modules
4. System design
5. Implementation
6. Results
7. Conclusion and future scope
Chapter 4. An intelligent remote diagnostic approach for diabetes using machine learning techniques
1. Introduction
2. Intelligent remote diagnostic approach for diabetes
3. Conclusion
Chapter 5. Diagnosis of diabetic retinopathy in retinal fundus images using machine learning and deep learning models
1. Introduction
2. Dataset
3. Texture feature extraction
4. Transform based feature extraction
5. Dimensionality reduction
6. Classification
7. CNN-based deep learning algorithm for DR classification
8. Summary
Chapter 6. Diagnosis of diabetes mellitus using deep learning techniques and big data
1. Introduction
2. Literature review
3. Materials and methods
4. Results and discussions
5. Conclusion and future work
Section II. Glucose monitoring
Chapter 7. IoT and machine learning for management of diabetes mellitus
1. Introduction
2. IoT and machine learning in general
3. Rationale of integrating IoT and machine learning in management of diabetes
4. IoT and machine learning in diabetes
5. Proposed framework and methodology
6. Future of IoT and machine learning
7. Conclusions
Chapter 8. Prediction of glucose concentration in type 1 diabetes patients based on machine learning techniques
1. Introduction
2. Glucose management in type 1 diabetes
3. Machine learning in healthcare
4. Predicting glucose concentrations using machine learning
5. Linear regression
6. Support vector machines
7. Random forest models
8. Deep learning models
9. Conclusion
Chapter 9. ML-based PCA methods to diagnose statistical distribution of blood glucose levels of diabetic patients
1. Introduction
2. Related algorithms
3. Prediction of fasting blood glucose level based on KPCA-LSSVM
4. Experimental methods
5. Conclusions
Section III. Prediction of complications and risk stratification
Chapter 10. Overview of new trends on deep learning models for diabetes risk prediction
1. Introduction
2. Overview of DL
3. The identification of diabetes mellitus
4. Management of blood sugar
5. Complications and their diagnosis
6. An overview of DL methods in a Nutshell
7. Discussion
8. Conclusion
Chapter 11. Clinical applications of deep learning in diabetes and its enhancements with future predictions
1. Introduction
2. Artificial intelligence
3. Diagnosis of diabetes mellitus
4. Diabetes-related complications
5. Glucose measurement and prediction
6. Conclusion/future aspect
Chapter 12. Exploring machine learning techniques for feature extraction and classification of diabetes related medical data: A comprehensive review
1. Introduction
2. Literature review
3. Diabetes datasets
4. Preprocessing
5. Classification techniques
6. Comparative analysis and discussion
7. Proposed method
8. Conclusion
Chapter 13. Machine learning-based predictive model for type 2 diabetes mellitus using genetic and clinical data
1. Introduction
2. The genetic basis of T2DM
3. Different methods of genotype-associations to T2DM
4. Core pillars of artificial intelligence
5. Different ML-based predictive models in genomics
6. Comparison of performance in ML-based predictive models for clinical and genetic data in T2DM
7. Challenges of ML-models and future prospects utilizing phylogenetic data
8. Conclusions
Chapter 14. Applications of IoT and data mining techniques for diabetes monitoring
1. Introduction
2. Internet of Things
3. IoT architecture
4. Data mining
5. Data mining techniques
6. Cloud computing
7. IoT and data mining for diabetes monitoring
8. Deployment of IoT and data mining
9. Technological barriers
10. Conclusion and future aspects
Chapter 15. Decision-making system for the prediction of type II diabetes using machine learning techniques and data balancing
1. Introduction
2. Literature survey
3. Methods and methodology
4. Experimental results and discussion
5. Conclusion
Chapter 16. Comparative analysis of machine learning tools in diabetes prediction
1. Introduction
2. Proposed methodology
3. Results and discussion
4. Conclusion
Chapter 17. Data analytic models of patients dependent on insulin treatment
1. Introduction
2. The insulin–glucose regulation system in the human body
3. Glucose regulation, monitoring, and risk evaluation in the last century
4. Continuous glucose measurement and the artificial pancreas in the 21st century
5. Conclusion
Chapter 18. Prediction of diabetes using hybridization of Radial basis function network and Differential evolution based technique
1. Introduction
2. Literature review
3. Proposed methodology
4. Development of RBFN_DE hybrid model
5. Experimental study
6. Conclusion
Chapter 19. An overview of new trends on deep learning models for diabetes risk prediction
1. Introduction
2. Background machine learning and deep learning
3. Systematic literature review
4. Proposed methodology
5. Results and discussion
6. Conclusion
Section IV. Dialysis
Chapter 20. Progression and identification of heart disease risk factors in diabetic patients from electronic health records
1. Introduction
2. Observational cohort studies
3. Comparative drug studies
4. Machine learning techniques
5. Registry- or population-based genetic studies
6. The role of electronic health records: Documentation and beyond
7. Pitfalls
8. Conclusion
Chapter 21. An intelligent fog computing–based diabetes prediction system for remote healthcare applications
1. Introduction
2. Related work
3. Preliminaries and technical background
4. Services provided by the fog layer
5. Fog-assisted healthcare support system
6. Monitoring diabetic patients: A case study
7. Results and discussions
8. Limitations
9. Challenges
10. Future directions
11. Conclusion
Chapter 22. Artificial intelligence approaches for risk stratification of diabetic kidney disease
1. Introduction
2. Machine learning and deep learning
3. Random forest
4. Support vector machine
5. Logistics regression
6. Neural networks
7. Limitations of ML and DL
8. Discussion
9. Conclusion
Chapter 23. A state-of-the-art review on computational methods for predicting the occurrence of cardiac autonomic neuropathy
1. Introduction
2. Tests and indicators of CAN
3. AI-assisted diagnosis of CAN
4. Limitations and future directions
5. Conclusions
Chapter 24. Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients
1. Introduction: The comorbidity of diabetes and depression
2. Diagnosis, treatment, and management of diabetes-depression comorbidity: Gaps in automatic and standardized approaches
3. Machine learning and Internet of Things: Possible solutions?
4. Machine learning–based solutions
5. Other state-of-the-art methods
6. Role of IoT + ML solutions: Toward early diagnosis, individualized treatment, and long-term management
7. Limitations and future directions
8. Conclusions
Section V. Drug design and treatment response
Chapter 25. Enhancing diabetic maculopathy classification through a synergistic deep learning approach by combining convolutional neural networks, transfer learning, and attention mechanisms
1. Introduction
2. Literature review
3. Proposed model for enhancing diabetic maculopathy classification through a synergistic deep learning approach by combining convolutional neural networks, transfer learning, and attention mechanisms
4. Result and comparison
5. Conclusion and future scope
Chapter 26. Pharmacogenomics: The roles of genetic factors on treatment response and outcomes in diabetes
1. Introduction
2. Metformin
3. Sulfonylureas
4. Thiazolidinediones
5. SGLT2 inhibitors
6. GLP-1 receptor agonists
7. DPP-4 inhibitors
8. Alpha-glucosidase inhibitors
9. Current challenges and future perspectives
Chapter 27. An up-to-date systematic review on machine learning approaches for predicting treatment response in diabetes
1. Introduction
2. Research strategy
3. Results
4. Discussion
Chapter 28. Antidiabetic potential of mangrove plants: An updated review
1. Introduction
2. Mode of antidiabetic activity of mangrove plant extracts
3. Phytochemicals of mangrove plants with antidiabetic properties
4. Antidiabetic studies on mangrove plants
5. Conclusion
Index

Sujata Dash

Sujata Dash holds the position of Professor at the Information Technology School of Engineering and Technology, Nagaland University, Dimapur Campus, Nagaland, India, bringing more than three decades of dedicated service in teaching and mentoring students. She has been honoured with the prestigious Titular Fellowship from the Association of Commonwealth Universities, United Kingdom. As a testament to her global contributions, she served as a visiting professor in the Computer Science Department at the University of Manitoba, Canada. With a prolific academic record, she has authored over 200 technical papers published in esteemed international journals, and conference proceedings, and edited book chapters by reputed publishers Serving as a reviewer and Associate Editor for approximately 15 international journals.

Affiliations and expertise

Department of Information Technology, School of Engineering and Technology, Nagaland University, India

Subhendu Kumar Pani

Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 years of teaching and research experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI.

Affiliations and expertise

Krupajal Engineering College, Prashanti Vihar, Near CIFA, Kausalya Ganga, Bhubaneswar, Khordha, Odisha, India

Willy Susilo

Willy Susilo received his Ph.D. degree in Computer Science from the University of Wollongong, Australia. He is a Distinguished Professor the Head of the School of Computing and Information Technology and the director of the Institute of Cybersecurity and Cryptology (iC2) at the University of Wollongong. Recently, he was awarded an Australian Laureate Fellowship, which is the most prestigious award in Australia, due to his contribution in cloud computing security. He was previously awarded a prestigious ARC Future Fellow by the Australian Research Council (ARC) and the Researcher of the Year award in 2016 by the University of Wollongong. He is a Fellow of IEEE, Australian Computer Society (ACS), IET and AAAI. His main research interests include cybersecurity, cryptography and information security. His work has been cited more than 25,000 times in Google Scholar. He is the Editor-in-Chief of the Elsevier Computer Standards and Interfaces and the MDPI Information journal. He has served as a program committee member in dozens of international conferences. He is currently serving as an Associate Editor in several international journals, including IEEE Transactions in Dependable and Secure Computing. Previously, he has served in many top-tier journals, such as IEEE Transactions in Information Forensics and Security. He has published more than 500 research papers in the area of cybersecurity and cryptology.

Affiliations and expertise

Director, Institute of Cybersecurity and Cryptology, Professor and Head of School of Computing and Information Technology, The University of Wollongong, Australia

Cheung Man Yung Bernard

Bernard Cheung went to Sevenoaks School and studied Medicine at the University of Cambridge. He was Professor of Clinical Pharmacology and Therapeutics at the University of Birmingham before returning to Hong Kong and being appointed the Sun Chieh Yeh Heart Foundation Professor in Cardiovascular Therapeutics. He was a Consultant Physician of Queen Mary Hospital and the Director of the Phase 1 Clinical Trials Units in Queen Mary Hospital and the University of Hong Kong-Shenzhen Hospital. Currently, he is the Biotechnology Director in the Innovation and Technology Commission. He is also the President of the Federation of Medical Societies of Hong Kong and the Editor-in-Chief of Postgraduate Medical Journal. Prof Cheung’s main research interest is in cardiovascular diseases and risk factors, including hypertension and the metabolic syndrome.

Affiliations and expertise

Sun Chieh Yeh Heart Foundation Professorship in Cardiovascular Therapeutics, heads the Division of Clinical Pharmacology and Therapeutics in the Department of Medicine of the University of Hong Kong

Gary Tse

Gary Tse is a distinguished academic physician-scientist and Professor at the School of Nursing and Health Sciences, Hong Kong Metropolitan University. Appointed to a full professorship in 2019 at Tianjin Medical University's Department of Cardiology, he also holds a joint appointment as Clinical Reader in Public Health Medicine at Kent and Medway Medical School, University of Kent, and serves as Public Health Consultant at the Medical Council's Public Health Directorate. Since 2021, he has been a Visiting Professor at the University of Surrey and Honorary Associate Professor at University College London. An elected member of the European Academy of Sciences and Arts since 2024, Tse has over 200 publications and an H-index of 58. He has secured more than HK$88 million in research funding and supervised 19 doctoral and 13 master students. His research focuses on using big data for cardiovascular risk prediction and developing AI-driven models for chronic diseases.

Affiliations and expertise

Distinguished Academic Physician-Scientist and Professor, School of Nursing and Health Sciences, Hong Kong Metropolitan University

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

Use cases

Purchase options

Save 50% on book bundles

Institutional subscription on ScienceDirect

Sujata Dash

Subhendu Kumar Pani

Willy Susilo

Cheung Man Yung Bernard

Gary Tse