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Table 2 Predictive power (in terms of percent accuracy) of several feature selection methods combined with different classification models. AUC results are shown in brackets

From: PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine

Prediction model

PCM-SABRE pipeline

Chou et al. [10]

Feature selection

InfoGain

ANOVA

MW U test

MW U test

RF

76.52 (NA)

77.70 (NA)

76.10 (NA)

NA

LR

76.27 (73.0)

66.55 (62.49)

75.68 (70.95)

64.12 (58.96)

PNN

76.52 (74.09)

76.27 (75.21)

74.58 (72.32)

69.54 (63.88)

KNN

75.76 (67.78)

75.34 (68.48)

76.10 (70.30)

NA

SVM

72.64 (NA)

72.64 (NA)

72.64 (NA)

NA

DT

70.19 (60.59)

68.07 (61.53)

64.44 (57.34)

63.45 (56.90)

DL

NA

NA

75.34 (71.71)

68.90 (61.66)

DA

NA

NA

75.51 (72.23)

65.91 (61.65)