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Table 2 Average and standard deviation of metrics of models with 5-fold cross-subject validation

From: A machine-learning approach to predict postprandial hypoglycemia

Model Sen (%,SD) Spe (%,SD) F1 score (SD) AUC (SD) NH (SD) TPe (SD) FAR (SD) DT (min,SD)
RF 89.6 91.3 0.543 0.966 36.4 30.2 0.704 25.5
  (2.78) (2.03) (0.053) (0.007) (11.0) (8.42) (0.035) (1.97)
SVM 93.3 88.2 0.487 0.967 36.4 29.2 0.777 25.8
-LN (1.70) (2.83) (0.046) (0.007) (11.0) (8.30) (0.034) (2.12)
SVM 89.9 88.8 0.487 0.952 36.4 29.4 0.760 25.2
-RBF (8.65) (2.96) (0.062) (0.014) (11.0) (9.20) (0.038) (3.22)
KNN 88.5 89.4 0.492 0.917 36.4 29.6 0.779 25.8
  (1.93) (2.09) (0.054) (0.012) (11.0) (8.73) (0.038) (3.76)
LR 93.6 87.9 0.484 0.967 36.4 29.6 0.772 25.0
  (2.25) (2.95) (0.047) (0.007) (11.0) (8.71) (0.037) (2.87)
  1. With the 5-fold cross-subject validation, average metrics were calculated using Eq 6, 7, 9, and 10 on test setq,q=1,2,3,4,5. Since there should be at least two consecutive predictions of a hypoglycemia alert value to make an alarm, we excluded hypoglycemic events occurring immediately after meals. Abbreviation: RF, random forest; SVM-LN, support vector machine with a linear kernel; SVM-RBF, support vector machine with a radial basis function; KNN, K-nearest neighbor; LR, logistic regression; SD, standard deviation; Sen, sensitivity; Spe, specificity; AUC, the area under the ROC curve; NH, the number of hypoglycemic events; FAR, false alarm rate; DT, detection time.