An accurate glucose detection platform using colorimetry and supervised learning algorithms

Maintaining optimal health and preventing diabetes-related complications requires accurate and timely monitoring of blood glucose levels. In line with this, the present study focuses on developing an affordable, reliable, and precise Point-of-Care (POC) diagnostic platform for glucose detection by integrating microfluidic and colorimetric principles. The system employs a custom-fabricated microfluidic chip designed to facilitate efficient enzymatic color reactions using only ~20 μl of sample per microwell, achieving complete color development within 3–4 min. This chip is housed inside a compact, USB-powered 3D-printed imaging module equipped with a high-resolution fixed-focus camera, enabling consistent control over imaging parameters such as focal distance, camera alignment, and illumination conditions. The overall workflow is optimized for seamless compatibility with embedded systems or laptops, eliminating the dependency on smartphones or external calibration tools and making the setup well-suited for real-time diagnostic use in POC environments. A total of 1280 images, representing 16 glucose concentration levels ranging from 50 to 200 mg dl⁻¹, were captured under standardized conditions, labelled according to known concentrations, and processed through uniform preprocessing steps. Engineered image features extracted from the preprocesses images were then analysed using supervised machine learning models, including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and a Feedforward Neural Network, to establish a robust predictive framework capable of delivering fast, consistent, and accurate glucose estimation for practical healthcare applications. Among the evaluated models, the Random Forest (RF) classifier achieved the highest cross-validation precision of 98% and an exceptional specificity approaching 100%. This clearly describes its ability to distinguish between different glucose concentration levels. Further, the confusion matrix and the ROC curve analysis have validated the model’s reliability, with very minimal chances of misclassifications and a high mean AUC value of around 1. These results ensure the potential of the image-based glucose concentration estimation as a cost effective and a reliable, scalable solution for real time monitoring in various medical related industries.