| Title |
Diagnosis of Non-invasive Glucose Monitoring by Integrating IoT and Machine Learning |
| Authors |
(V. K. R. Rajeswari Satuluri) ; (Vijayakumar Ponnusamy) |
| DOI |
https://doi.org/10.5573/IEIESPC.2022.11.6.435 |
| Keywords |
Artificial intelligence; Diabetes mellitus; Deep learning; NIR |
| Abstract |
Diabetes Mellitus (DM) is a term collectively used for all types of diabetes. DM increases the risk factor for health complications if not treated early. The Internet of Things (IoT) and artificial intelligence (AI) in healthcare have become a huge benefit for managing DM. The selfsupervision of healthcare has become convenient because of IoT-enabled devices. This paper reviews the management of diabetes, such as invasive, non-invasive, and minimally invasive methods. Justification for the need for non-invasive monitoring of glucose is discussed. Different AI and IoT-enabled management for non-invasive diabetes are also briefed. This review aims at the type of machine learning algorithms applied to non-invasive glucose monitoring. The following are to be considered to achieve an effective non-invasive method of monitoring glucose: Near Infrared spectroscopy (NIR) and Machine learning algorithms(ML). IoT in glucose monitoring has empowered doctors and caretakers to deliver outstanding care. Self-care by every person has become essential, which can be achieved by handheld or wearable IoT devices. Using current technologies, the possibility of making a wearable to monitor the glucose level is becoming closer to reality and has enormous potential. |