Optimizing Real-Time Glucose Monitoring: Addressing Sensor Drift in Continuous Glucose Meters Using Machine Learning Algorithms

Abstract

The rising incidence of diabetes necessitates accurate and reliable continuous glucose monitoring (CGM) systems. This study tackles a significant challenge in CGM technology: sensor drift, which compromises the accuracy of blood glucose readings. We utilized a comprehensive dataset encompassing over 50,000 glucose readings collected from diverse patient populations over six months. Our methodological framework employed advanced machine learning algorithms, specifically Support Vector Machines and Neural Networks, to model and correct sensor drift, ultimately enhancing the precision of CGM devices. Quantitative evaluation metrics demonstrated a reduction in error rates by 25% compared to conventional calibration methods. The findings underscore the importance of integrating machine learning techniques in the advancement of CGM technology, offering a promising direction for improving diabetes management and patient outcomes.