Mealtime Blood Glucose Classifier Based on Fuzzy Logic for the DIABTel Telemedicine System

Authors:
Gema García-Sáez;José M. Alonso;Javier Molero;Mercedes Rigla;Iñaki Martínez-Sarriegui;Alberto Leiva;Enrique J. Gómez;M. Elena Hernando
Affiliations:
Bioengineering and Telemedicine Centre, Politechnical University of Madrid, CIBER-BBN Networking Research Centre, Spain;European Centre for Soft Computing, Mieres (Asturias), Spain;Bioengineering and Telemedicine Centre, Politechnical University of Madrid, CIBER-BBN Networking Research Centre, Spain;Endocrinology Dept., Hospital de Sabadell, CIBER-BBN Networking Research Centre, Spain;Bioengineering and Telemedicine Centre, Politechnical University of Madrid, CIBER-BBN Networking Research Centre, Spain;Endocrinology Dept., Hospital Sant Pau, CIBER-BBN Networking Research Centre, Barcelona, Spain;Bioengineering and Telemedicine Centre, Politechnical University of Madrid, CIBER-BBN Networking Research Centre, Spain;Bioengineering and Telemedicine Centre, Politechnical University of Madrid, CIBER-BBN Networking Research Centre, Spain
Venue:
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Year:
2009

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Abstract

The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge Management (KM) tools that support decision-making in a telemedicine system. This work presents a fuzzy rule-based classifier integrated in a KM agent of the DIABTel telemedicine architecture, to automatically classify BG measurements into meal intervals and moments of measurement. Fuzzy Logic (FL) tackles with the incompleteness of BG measurements and provides a semantic expressivity quite close to natural language used by physicians, what makes easier the system output interpretation. The best mealtime classifier provides an accuracy of 77.26% and does not increase significantly the KM analysis times. Results of classification are used to extract anomalous trends in the patient's data.