Skip to main content
. 2012 Sep 28;6:69. doi: 10.3389/fnsys.2012.00069

Table 3.

Training Dataset results: accuracies for binary and three-way diagnosis using various classifiers with personal characteristic data from the 668 participant Training Dataset.

Diagnostic task Input data Classifier Accuracy (%) P value
Binary Chance 64.2 ± 3.8
Personal characteristic Logistic 73.7 ± 5.1 1 × 10−5
data selection 1 (PCs1) Linear SVM 74.4 ± 4.6 9 × 10−6
Quadratic SVM 74.5 ± 6.2 4 × 10−5
Cubic SVM 74.4 ± 4.8 2 × 10−6
RBF SVM 64.2 ± 3.8 NA
Personal characteristic Logistic 74.0 ± 5.0 1 × 10−5
data selection 2 (PCs2) Linear SVM 75.0 ± 4.5 7 × 10−6
Quadratic SVM 74.2 ± 6.3 5 × 10−5
Cubic SVM 73.8 ± 5.0 3 × 10−5
RBF SVM 64.2 ± 3.8 NA
Three-way Chance 64.2 ± 3.8
Personal characteristic Logistic 68.7 ± 8.1 0.020
data selection 1 (PCs1) Linear SVM 66.9 ± 7.1 0.038
Quadratic SVM 68.6 ± 7.5 0.006
Cubic SVM 68.6 ± 8.2 0.020
RBF SVM 64.2 ± 3.8 NA
Personal characteristic Logistic 69.0 ± 8.3 0.020
data selection 2 (PCs2) Linear SVM 66.9 ± 7.2 0.050
Quadratic SVM 68.9 ± 7.9 0.009
Cubic SVM 67.5 ± 7.7 0.046
RBF SVM 64.2 ± 3.8 NA

PCs1, personal characteristic data selection 1; PCs2, personal characteristic data selection 2. See section 2.3 for details. Accuracy column shows mean accuracies ± standard deviations across 10-fold cross validation. Best result in a given category is shown in bold font. P value column shows p values for one-tailed, paired samples t-tests (df = 9) of accuracies against chance baseline (guessing control for every participant). NA (not available) indicates that the t-test was undefined in the case of SVM (RBF), which always guessed control and produced predictions identical to chance baseline.