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. 2014 Jul 14;6:145. doi: 10.3389/fnagi.2014.00145

Table 5.

Performance and prediction based on the AD vs. CTL classification model.

AD-like CTL-like Sensitivity (%) Specificity (%) AUC
TRAINING SET (BASELINE, N = 348)*
AD 100 19 84 91 0.93 ± 0.02
CTL 10 100
LONGITUDINAL DATASET (BASELINE, N = 214)**
AD 50 12 81 90 0.90 ± 0.03
CTL 8 71
MCI-c 11 2 85 65 0.80 ± 0.08
MCI-nc 21 39
LONGITUDINAL DATASET (1 YEAR, N = 214)
AD 57 5 92 75 0.93 ± 0.02
CTL 20 59
MCI-c 12 1 92 47 0.75 ± 0.09
MCI-nc 32 28

A training set was used to train a classifier and applied on the longitudinal dataset; see Figure 1 for a description of the datasets. AD, Alzheimer disease; CTL, healthy control. MCI, mild cognitive impairment; MCI-c, MCI converters; MCI-nc, MCI non-converters; AUC, area under the receiver operating characteristic curve.

*

Training model using cross-validation.

**

Values recalculated from the baseline cross-validated model.