A Comparative Analysis of Unscented Kalman Filter for Smartphone-Based Multi-Parametric Glucose Prediction

Authors : Ece Ayfer, Ömer Atılım Koca, Volkan Kılıç

Pages : 1-11

View : 110 | Download : 50

Publication Date : 2025-06-27

Article Type : Research Paper

Abstract :Diabetes mellitus, a chronic disease affecting millions of people worldwide, requires monitoring and management of glucose levels to reduce the risks of hyperglycemia and hypoglycemia. Technological advancements have enabled the development of various digital tools, including continuous glucose monitors (CGMs) for effective management of this disease. However, these tools only provide alerts after glucose levels exceed critical thresholds, which causes delays in taking necessary precautions. To address this issue, various artificial intelligence (AI)-based models have been developed to predict glucose levels in advance. Traditional AI approaches, however, often rely on standardized datasets, limiting their ability to achieve the accuracy required for individualized treatment. Therefore, it is crucial to develop personalized prediction models that can be trained using the individual data of patients. Here, this paper introduces a personalized glucose prediction approach that employs a three-parameter unscented Kalman filter (UKF) to predict future glucose levels using CGM data, as well as basal and bolus insulin values. Experiments on OhioT1DM dataset show the advantage of our proposed approach over the baseline KF and UKF for glucose prediction in terms of Root Mean Square Error. Furthermore, the proposed approach is embedded into a custom-designed cross-platform smartphone application, GlucoThinker Advance, capable of providing offline access to the proposed personalized glucose prediction approach to ensure continuous support without requiring an internet connection.
Keywords : Glikoz Tahmini, Çok-Parametre, Kişiselleştirilmiş, Akıllı Telefon Uygulaması, Kokusuz Kalman Filtresi

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