Table 2: Results of testing differences in prediction accuracies between the created ANN, and MLR models
|ANN model||ANN architecture||MSE on the test sample||SMAPE on the test sample (%)|
|Fasting glucose variability||2 hidden layers||5.23||23.69|
|Postprandial glucose variability||1 hidden layer||30.58||33.24|
|Increased HbA1c||1 hidden layer||0.17||1.47|
ANN: Artificial neural networks; HbA1c: glycosylated hemoglobin A1c; MSE: mean squared error; SMAPE: symmetric mean average percentage error.
|MLR model||Model's size||MSE on the test sample||SMAPE on the test sample (%)|
|Fasting glucose variability||number of explanatory variables = 14**||2.47||69.50|
|Postprandial glucose variability||number of explanatory variables = 10||6.79||99.64|
|Increased HbA1c||number of explanatory variables = 16||1.66||13.44|
MLR: Multiple linear regression; **the variables "Dg of the upper gastrointestinal tract disorders" and "Antidiabetic drugs in use > 6 months" was split into the two components.