Table 2 Cross-validated goodness-of-fit metrics for linear classifiers.

From: Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics

 

AUC

Sensitivity

Specificity

Accuracy

Precision

Recall

F1-score

Training size

#Features

MKL-Linear

0.90(0.02)

0.86(0.03)

0.78(0.03)

0.80(0.02)

0.63(0.03)

0.86(0.03)

0.72(0.03)

80%

5

SVM-Linear

0.93(0.02)

0.93(0.03)

0.77(0.03)

0.81(0.02)

0.64(0.03)

0.93(0.02)

0.75(0.03)

80%

65

GLM-Elastic Net

0.92(0.02)

0.91(0.02)

0.76(0.02)

0.81(0.02)

0.62(0.03)

0.91(0.04)

0.74(0.03)

80%

25

  1. Here we list the goodness-of-fit metrics (AUC, sensitivity, specificity, accuracy, precision, recall, and F1-score) obtained for the test dataset (20% of the whole dataset), using the subset of features that provided the most generalizable result, as shown in Fig. 4. Their average values and standard deviations were computed using a tenfold stratified cross-validation.
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