Implementation of a Novel Personalized Non-Invasive Glucometer Using Machine Learning

In recent years, diabetes has become more prevalent, and patients suffer from pain and infection due to invasive procedures. Non-invasive glucose monitoring devices improve patient comfort by reducing pain and infection compared to invasive methods. This paper presents a novel non- invasive glucose monitoring system by employing NIR technology and a machine learning algorithm to provide an accurate and continuous monitoring system that simultaneously displays a result on a mobile app in a user-friendly way. This can lead to an optimized system than conventional methods that require blood samples, which is significant for diabetic therapy. By incorporating the proposed technology, a non-invasive as well as user-friendly system is implemented that improves the quality of life of diabetic patients with the potential to completely transform glucose monitoring, reduce the burden on diabetics, and provide healthcare professionals with a valuable tool for improved patient care.