A predictive model of subcutaneous glucose concentration in type 1 diabetes based on Random Forests.
In this study, an individualized predictive model of the subcutaneous glucose concentration in type 1 diabetes is presented, which relies on the Random Forests regression technique. A multivariate dataset is utilized concerning the s.c. glucose profile, the plasma insulin concentration, the intestinal absorption of meal-derived glucose and the daily energy expenditure. In an attempt to capture daily rhythms in glucose metabolism, we also introduce a time feature in the predictive analysis. The dataset comes from the continuous multi-day recordings of 27 type 1 patients in free-living conditions. Evaluating the performance of the proposed method by 10-fold cross validation, an average RMSE of 6.60, 8.15, 9.25 and 10.83 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively, was attained.
Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR 45110 Greece. egeorga@cs.uoi.gr
Eleni I
EI
Georga
Vasilios C
VC
Protopappas
Demosthenes
D
Polyzos
Dimitrios I
DI
Fotiadis
D000328:Adult; D000368:Aged; D003922:Diabetes Mellitus, Type 1 / Q000378:metabolism*; D005260:Female; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical
D005947:Glucose