Data Modelling and Analytics for the Internet of Medical Things

The emergence of the Internet of Medical Things (IoMT) is transforming the management of diseases, improving diseases diagnosis and treatment methods, and reducing healthcare costs and errors. This book covers all the essential aspects of IoMT in one place, providing readers with a comprehensive grasp of IoMT and related technologies.

Data Modelling and Analytics for the Internet of Medical Things integrates the architectural, conceptual, and technological aspects of IoMT, discussing in detail the IoMT, connected smart medical devices, and their applications to improve health outcomes. It explores various methodologies and solutions for medical data analytics in healthcare systems using machine learning and deep learning approaches, as well as exploring how technologies such as blockchain and cloud computing can further enhance data analytics in the e-health domain. Prevalent IoMT case studies and applications are also discussed.

This book is suitable for scientists, design engineers, system integrators, and researchers in the field of IoMT. It will also be of interest to postgraduate students in computer science focusing on healthcare applications and a supplementary reading for IoMT courses.

Part I. IoMT Datasets and Storage. 1. Remote Health Monitoring in the Era of the Internet of Medical Things. 2. Diabetic health care data analytics and application. 3. Blockchain for Handling Medical Data. 4. Cloud computing for complex IoMT data. 5. The potential of IoMT Devices in Early Detection of Suicidal Ideation. Part II.Machine Learning for Medical Things. 6. Artificial Intelligence and Internet of Medical Things in the Diagnosis and Prediction of Disease. 7. Predicting Cardiovascular Diseases Using Machine Learning: A Systematic Review of the Literature. 8. Identification of Unipolar Depression Using Boosting Algorithms. 9. Development of EEG based Identification of Learning Disability using Machine Learning Algorithms. 10. Deep Learning Approaches for IoMT. 11 Machine Learning and Deep Learning Techniques to Classify Depressed Patients from Healthy, Using Brain Signals from Electroencephalogram (EEG). 12. Dimensionality Reduction for IoMT Devices Using PCA. 13. Face Mask Detection System. Part III. IoMT: Data Analytics and Use Cases. 14. An IoT-based Real-time ECG Monitoring Platform for Multiple Patients. 15. Study on Anomaly Detection in Clinical Laboratory Data Using Internet of Medical Things. 16. Computational Intelligence Framework for Improving Quality of Life in Cancer Patients. 17. Major Depressive Disorder Detection using Data Science and Wearable Connected Devices.

Rajiv Pandey, Senior Member IEEE, is a faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India.

Pratibha Maurya is an assistant professor at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India.

Raymond Chiong is currently an associate professor with the University of Newcastle, Australia.