The overall day 1 mean absolute relative accuracy (ARD) of the Dexcom G4 sensor (Dexcom) was 10.4 ± 9.1% with a median of 7.7% (4.7, 13.6) (n= 201).
The use of four sensors significantly reduced very large errors compared with one sensor (0.4 vs. 2.6% of errors ≥50% from reference glucose, P < 0.001) and also improved overall accuracy (mean absolute relative difference, 11.6 vs. 14.8%, P < 0.001).
This table shows the Accuracy (ACC), mean absolute error (MAE), and weighted average F-score (WFS) for k-nn, Naive Bayes, and logistic regression, using leave one out cross-validation.
Sensor accuracy, defined as mean absolute relative deviation, was 10.6 ± 1.6%.
Sensor accuracy, defined as mean absolute relative deviation between sensor and reference blood glucose levels, was 10.1 ± 2.0% during the CL studies.
By applying the hybrid refinement scheme on a well-known corpus, significant improvements of segmentation results in terms of segmentation accuracy with different tolerances, mean absolute error (MAE), and root-mean-square error (RMSE) can be observed.
Measures of prediction accuracy, which included bias, mean absolute error (MAE) and root mean square error (RMSE), 24 were calculated based on 10-fold cross-validation.
Most of the improvements were at the slight to moderate levels, e.g., 3.66 14.24% increases in the accuracy based on mean error, mean absolute error, root mean square error and R2.
Sensor accuracy was calculated as the mean absolute relative deviation of the sensor glucose level from the reference venous glucose level for all paired points.
