An AI-Based Non-Invasive Solution for Predicting Glucose Levels

Abstract

An AI-based non-invasive solution for predicting glucose levels aims to eliminate the discomfort, cost, and inconvenience associated with traditional invasive blood glucose monitoring methods. Conventional techniques require frequent finger-prick tests or implanted sensors, which can be painful and discourage regular monitoring. The proposed approach leverages artificial intelligence and machine learning models to analyze non-invasive physiological signals such as skin temperature, heart rate variability, optical signals, sweat composition, and infrared or image-based data. By learning complex patterns between these signals and glucose levels, the system can accurately predict blood glucose trends in real time. This solution enhances patient comfort, enables continuous monitoring, and supports early detection and effective management of diabetes.

Existing System

The existing glucose monitoring systems primarily rely on invasive techniques such as finger-stick glucometers and Continuous Glucose Monitoring (CGM) devices that require subcutaneous sensor insertion. Although CGM systems provide continuous readings, they are expensive, require periodic sensor replacement, and may cause skin irritation or infection. These systems also depend on consumables like test strips and lancets, increasing long-term costs. Additionally, frequent invasive testing can reduce patient compliance, especially among children and elderly patients. Non-invasive alternatives exist but often lack accuracy, reliability, or real-time predictive capability due to limited analytical intelligence.

Proposed System

The proposed system introduces an AI-driven, fully non-invasive glucose prediction framework using machine learning and sensor-based data acquisition. Physiological parameters such as photoplethysmography (PPG), skin impedance, thermal data, and lifestyle inputs (diet, activity, sleep) are collected through wearable or camera-based devices. Advanced machine learning models—such as Support Vector Machines (SVM), Random Forests, LSTM networks, or deep neural networks—analyze these inputs to predict glucose levels and trends. The system continuously learns from historical data to improve prediction accuracy over time. A user-friendly mobile or web interface displays glucose predictions, alerts for abnormal levels, and personalized health insights. This AI-based non-invasive solution enhances comfort, reduces healthcare costs, improves patient adherence, and serves as an effective decision-support tool for diabetes management.