Classification of glucose-level in deionized water using machine learning models and data pre-processing technique

Accurate monitoring of glucose levels is essential in the field of diabetes detection and prevention to ensure appropriate treatment planning. Conventional blood glucose monitoring methods, although widely used, are intrusive and frequently result in discomfort. This study investigates the use of Raman spectroscopy as a non-invasive method for estimating glucose concentrations. Our proposition entails employing machine learning models to categorize glucose levels by utilizing Raman spectrum data. The collection consists of deionized water samples containing glucose with defined amounts, guaranteeing great purity and little interference. We assess the efficacy of three machine learning models in categorizing glucose levels which including Extra Trees, Random Forest, and Support Vector Machine (SVM). In addition, we employ data pre-processing techniques such as fluorescence background removal and hotspot series extraction to improve the performance of the model. The primary results demonstrate that the utilization of these pre-processing techniques greatly enhances the accuracy of classification. Among these techniques, the Extra Trees model achieves the highest accuracy, reaching 95%. This study showcases the viability of employing machine learning techniques to forecast glucose levels based on Raman spectroscopy data. Additionally, it emphasizes the significance of data pre-processing in enhancing the accuracy of the model's results.