Review on Non-Invasive Electromagnetic Approaches for Blood Glucose Monitoring Using Machine Learning
- Resource Type
- Conference
- Authors
- Mansour, Esraa; Shahin, Mohamed; Ashraf, Abdelrahman; Atta, AbdelAzim; Allam, Ahmed; Zewail, Rami; Abdel-Rahman, Adel B.
- Source
- 2023 11th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Electronics, Communications, and Computations (JAC-ECC), 2023 11th International Japan-Africa Conference on. :273-276 Dec, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Signal Processing and Analysis
Training
Reviews
Computational modeling
Machine learning
Transforms
Predictive models
Microwave communication
Machine Learning (ML)
Non-invasive Glucose Monitoring Sensors
Debye Model
CSRR
Metamaterial
SVM
Linear Regression
Decision trees
Random Forest
- Language
This paper particularly explores the microwave technique, reviewing current research and its limitations in non-invasive blood glucose monitoring applications. It also examines how using artificial intelligence can make predicting blood sugar levels more precise. Combining machine learning with current technology could transform diabetes care, making it easier and more effective for patients worldwide.