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Table 2 Classification results using discriminant RBM, PCA with SVM, and LDA (Bold font denotes the best performance on certain metric of the 4 patients)

From: PATTERN: Pain Assessment for paTients who can’t TEll using Restricted Boltzmann machiNe

Patient ID Model AUC Sensitivity Specificity Accuracy
7137 RBM 0.73255 0.7143 0.6286 0.6714
PCA + SVM 0.65541 0.8143 0.4714 0.6429
LDA 0.65316 0.8 0.4858 0.6429
4822 RBM 0.76017 0.7037 0.6885 0.6932
PCA + SVM 0.67729 0.6667 0.6557 0.625
LDA 0.67122 0.7037 0.5902 0.6591
1245 RBM 0.8813 0.8667 0.8049 0.8214
PCA + SVM 0.76504 0.8667 0.7317 0.7679
LDA 0.82033 0.8 0.7561 0.7679
6563 RBM 0.78594 0.7692 0.6721 0.7011
PCA + SVM 0.71721 0.5769 0.6885 0.6552
LDA 0.69893 0.7308 0.6393 0.6667