Internet of Things and Machine Learning for Type I and Type II Diabetes
Use cases
- 1st Edition - July 15, 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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Request a sales quoteInternet 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.
- Integrates many Machine learning techniques in biomedical domain to detect various types of diabetes to utilizing large volumes of available diabetes-related data for extracting knowledge
- It integrates data mining and IoT techniques to monitor diabetes patients using their medical records (HER) and administrative data
- Includes clinical applications to highlight contemporary use of these machine learning algorithms and artificial intelligence-driven models beyond research settings
Researchers and practitioners working in the biomedical field, diabetes, bioengineering, health informatics, bioelectronics, medical electronics, PhD students in life sciences and computer science
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
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
- No. of pages: 500
- Language: English
- Edition: 1
- Published: July 15, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780323956864
SD
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 such as Springer, Elsevier, IEEE, IGI Global USA, and Wiley. Dr. Dash boasts ten patents, two copyrights, numerous textbooks, and edited books to her credit. Actively engaged in professional associations, she is a life member of renowned international bodies like ACM, IRSS, CSI, IMS, OITS, OMS, IACSIT, and IST, and holds a Senior membership in IEEE. Serving as a reviewer and Associate Editor for approximately 15 international journals, including prestigious publications like World Scientific, Bioinformatics, Springer, IEEE ACCESS, Inderscience, and Science Direct, she significantly contributes to the scholarly review process. Dr. Dash has been honoured with various national and international awards and serves on the editorial boards of around ten international journals. Her global presence extends to delivering keynote speeches, invited talks, and chairing special sessions at international conferences in India and overseas. Her research expertise encompasses Biomedical and Healthcare, Machine Learning, Deep Learning, Data Science, Big Data Analytics, Bioinformatics, and Intelligent Agents.
Affiliations and expertise
Department of Computer Application, Maharaja Srirama Chandra BhanjaDeo University (formerly North Orissa University), Baripada, Mayurbhanj, Odisha, IndiaSP
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, IndiaWS
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, AustraliaCY
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 KongGT
Gary Tse
Professor Gary Tse received his Bachelor of Arts with Honours (B.A.) from the University of Cambridge (2008), Master of Arts (M.A.) from the University of Cambridge (2012), Bachelor of Medicine and Bachelor of Surgery (M.B.B.S.) from Imperial College London (2014), Doctor of Philosophy (Ph.D.) from the University of Cambridge (2015), Master in Public Health (M.P.H.) from the University of Manchester (2017), Master of Health Management (M.H.M.) from the University of New South Wales (2019), Doctor of Medicine (M.D.) from the University of Cambridge (2022) and Doctor of Medicine (D.M.) from the University of Oxford (2022). He was elected a Fellow of the European Society of Cardiology (2016), Fellow of the American College of Cardiology (2017), Heart Rhythm Society (2018), Royal College of Physicians of London (2019), Royal College of Paediatrics and Child Health (2019), Royal College of Pathologists (2022) and Royal College of Physicians of Ireland (2023). He serves as a Nucleus Committee Member of the Population Health Section, European Association of Preventive Cardiology.
In 2019, he was appointed to a full professorship at the Department of Cardiology, The Second Hospital, Tianjin Medical University, serving as a Principal Investigator, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Tianjin, China. Concurrently, since 2021, he held a joint appointment as Clinical Reader in Public Health Medicine at the Kent and Medway Medical School, University of Kent and Canterbury Christ Church University (with appointment to the retirement age) and Public Health Consultant at the Public Health Directorate of Medical Council. In 2021, he was appointed Visiting Professor, Faculty of Health and Medical Sciences, University of Surrey, Guildford and Honorary Associate Professor, School of Pharmacy, University College London, London.
Currently, he is Professor and Associate Dean (Innovations and Research) at the School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China. He leads the Hong Kong Risk Modelling Team focusing on the use of big data for cardiovascular risk prediction. His team conducted a number of territory-wide studies on the development of artificial intelligence-driven predictive risk model for diabetes mellitus in Hong Kong.
Affiliations and expertise
Distinguished Professor in Cardiology, Tianjin Institute of Cardiology and Reader in Public Health, Kent and Medway Medical School, University of Kent and Canterbury Christ Church University, Kent, UK