Performance Measurements

Three classification performance measurements, i.e., accuracy (Acc), sensitivity (Sn), and specificity (Sp), were used to evaluate how well a feature subset performed (Ye et al., 2017; Xu et al., 2018; Yokoi et al., 2018; Zhao et al., 2018). The RA children were regarded as the positive samples (P) while the matched controls were the negative samples (N). P and N were also denoted as the numbers of positive and negative samples. Sensitivity (Sn) was defined as the correctly predicted ratio of positive samples, i.e., Sn = TP/(TP + FN) = TP/P, where TP and FN were the numbers of correctly and incorrectly predicted positive samples, respectively. Specificity (Sp) was the correct prediction ratio of negative samples, i.e., Sp = TN/(TN + FP) = TN/N, where TN and FP were the numbers of negative samples with correct and incorrect predictions, respectively. The overall prediction Acc was defined as Acc = (TP + TN)/(P + N).

These measurements were used in various prediction models like the DNA and RNA functional elements (He et al., 2018; Feng et al., 2019). And they were calculated using the 10-fold cross-validation (10FCV) strategy as similar in Ye et al. (2017) and Zhao et al. (2018).