Results of testing differences in prediction accuracies between the created ANN, and MLR models

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.