  Daytime hypoglycemia should be accurately predicted to achieve normoglycemia
and to avoid disastrous situations. Hypoglycemia, an abnormally low blood
glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia.
Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In
this paper, we propose new predictor variables to predict daytime hypoglycemia
using continuous glucose monitoring (CGM) data. We apply classification and
regression tree (CART) as a prediction method. The independent variables of our
prediction model are the rate of decrease from a peak and absolute level of the
BG at the decision point. The evaluation results showed that our model was able
to detect almost 80% of hypoglycemic events 15 min in advance, which was higher
than the existing methods with similar conditions. The proposed method might
achieve a real-time prediction as well as can be embedded into BG monitoring
device.
