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Impact of the glycaemic sampling method in diabetes data mining

Machado, D. ; Costa, V.S.C. ; Brandão, P.

Impact of the glycaemic sampling method in diabetes data mining, Proc IEEE International Symp. on Computers and Communications - ISCC, Rhodes, Greece, Vol. , pp. - , June, 2022.

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Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioT1DM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results.
Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.