1st Edition
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.
Dalal Alshehri, Nasimul Noman, Raymond Chiong and Shah J Miah
2. Diabetic health care data analytics and application.
Dev Kapadia, Pratyush Mohanty, Anupama Namburu and Aravapalli Rama Satish
3. Blockchain for Handling Medical Data.
T.Vigneswari, Farjana Farvin Sahapudeen and G.Kalaiselvi
4. Cloud computing for complex IoMT data.
Akrati Sharma and Priti Maheshwary
5. The potential of IoMT Devices in Early Detection of Suicidal Ideation.
Archana Sahai and Umar Khalid Farooqui
Part II. Machine Learning for Medical Things.
6. Artificial Intelligence and Internet of Medical Things in the Diagnosis and Prediction of Disease.
Narendra Kumar Sharma, Shahnaz Fatima and Alok Singh Chauhan
7. Predicting Cardiovascular Diseases Using Machine Learning: A Systematic Review of the Literature.
Abhay Kumar Pathak, Bhupendra Kumar Dewangan and Manjari Gupta
8. Identification of Unipolar Depression Using Boosting Algorithms.
Parul Verma, Roopam Srivastava and Shikha Srivastava
9. Development of EEG based Identification of Learning Disability using Machine Learning Algorithms.
Nitin Ahire, R.N. Awale and Abhay Wagh
10. Deep Learning Approaches for IoMT.
Farjana Farvin Sahapudeen, T.Vigneswari and S.Krishna Mohan
11. Machine Learning and Deep Learning Techniques to Classify Depressed Patients from Healthy, Using Brain Signals from Electroencephalogram (EEG).
Bethany Gosala, Gosala Emmanuel Raj and Manjari Gupta
12. Dimensionality Reduction for IoMT Devices Using PCA.
Rajiv Pandey, Radhika Awasthi, Archana Sahai and Pratibha Maurya
13. Face Mask Detection System.
Rajiv Pandey, Radhika Awasthi, and Archana Sahai
Part III. IoMT: Data Analytics and Use Cases.
14. An IoT-based Real-time ECG Monitoring Platform for Multiple Patients.
Vishnu S, K. Lova Raju, Shanmugam M and Sharma S
15. Study on Anomaly Detection in Clinical Laboratory Data Using Internet of Medical Things.
Richa Singh and Nidhi Srivastava
16. Computational Intelligence Framework for Improving Quality of Life in Cancer Patients.
Perla Sunanda and Dwaram Kavitha
17. Major Depressive Disorder Detection using Data Science and Wearable Connected Devices.
Umar Khalid Farooqui and Archana Sahai
Biography
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.
