GlucoBreath An IoT ML and Breath Based Non Inasive Glucose Meter

Diabetes management requires continuous monitoring of blood glucose levels to prevent severe health complications. Conventional glucose monitoring methods rely on invasive finger-prick techniques, which are often painful, inconvenient, and discourage frequent testing. To address these limitations, this paper presents GlucoBreath, a non-invasive glucose monitoring system that estimates blood glucose levels through breath analysis. The system detects acetone concentration in exhaled breath using MQ-series gas sensors, which correlates with glucose metabolism. A NodeMCU-based microcontroller processes the sensor data and transmits it to a cloud platform via IoT communication. Machine learning algorithms are employed to analyze the collected data and predict glucose levels with improved accuracy. The predicted values are displayed through a userfriendly mobile or web interface, enabling real-time monitoring and alert notifications for abnormal conditions. The proposed system is portable, cost-effective, and eliminates the need for blood sampling, thereby enhancing user comfort and compliance. Experimental results demonstrate that the system provides reliable glucose estimation with minimal latency, making it a promising solution for non-invasive diabetes management in both home and clinical environments..