A machine-learning approach to predict postprandial hypoglycemia

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

With the 5-fold cross-subject validation, average metrics were calculated using Eq 6, 7, 9, and 10 on test set_q,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.

ISSN: 1472-6947