Reinforcement Learning-Based Kalman Filtering for Glucose Prediction

Authors : Ömer Atılım Koca, Bengü Fetiler, Volkan Kılıç

Pages : 89-98

View : 64 | Download : 192

Publication Date : 2025-12-23

Article Type : Research Paper

Abstract :Accurate prediction of glucose levels in diabetes patients is critical for preventing complications such as hypoglycemia and hyperglycemia. In recent years, the implementation of continuous glucose monitoring (CGM) systems has enabled the development of prediction models. Among these models, Kalman filtering (KF) and its variants, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), have been widely applied for modeling both linear and nonlinear systems. However, these filtering models depend on fixed parameters, which limits their adaptability to changing physiological conditions and reduces their performance for long-term prediction. Recent advancements in machine learning enable continuous dynamic adaptation to changing conditions, providing an effective solution to these limitations. In particular, Q-Learning (QL), a reinforcement learning algorithm, can dynamically update model parameters based on environmental feedback, thereby enabling more accurate and personalized glucose predictions. This study investigates the glucose prediction performance of hybrid models that integrate KF, EKF, and UKF with QL algorithm. Experimental evaluations were conducted on the OhioT1DM dataset, using various parameter configurations across multiple prediction horizons ranging from 5 to 90 minutes. Results demonstrate that the standard KF provides high accuracy for short-term predictions, while the UKF shows superior performance for long-term prediction.
Keywords : Kalman Filtre, Q-Öğrenme, Pekiştirmeli Öğrenme, Glikoz Tahmini, Diyabet Yönetimi, Sürekli Glikoz İzleme

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