Table 2 Model performance (in percentage) and elements of confusion matrix of the various stacked learners in EMPaSchiz model: average (standard errors) − 5 × 10-fold CV

From: Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

 

Accuracy

Precision

Sensitivity

Specificity

True positive

True negative

False positive

False negative

Stacked-multi

86.9 (1.1)

91.9 (1.4)

79.8 (1.8)

93.1 (1.2)

65.0 (1.4)

86.8 (1.2)

6.2 (1.1)

16.0 (1.4)

Stacked-ALFF

76.4 (1.4)

76.3 (1.8)

73.9 (2.2)

78.7 (1.9)

59.8 (1.7)

73.0 (1.7)

20.0 (1.9)

21.2 (1.8)

Stacked-ReHo

74.1 (1.6)

73.4 (2.0)

74.6 (2.0)

73.6 (2.5)

60.4 (1.6)

68.2 (2.3)

24.8 (2.5)

20.6 (1.6)

Stacked-fALFF

74.5 (1.5)

73.8 (1.7)

72.2 (1.8)

76.6 (1.9)

58.6 (1.6)

72.0 (1.7)

21.0 (1.7)

22.4 (1.7)

Stacked-FC-correlation

82.4 (1.3)

83.9 (1.9)

79.7 (1.8)

84.7 (2.0)

64.6 (1.5)

78.8 (2.0)

14.2 (1.9)

16.4 (1.4)

Stacked-FC-partial correlation

78.5 (1.4)

93.7 (1.5)

58.2 (2.8)

96.2 (0.9)

46.8 (2.4)

89.8 (1.0)

3.2 (0.8)

34.2 (2.3)

Stacked-FC-precision

83.7 (1.2)

90.2 (1.6)

73.8 (2.0)

92.3 (1.3)

60.0 (1.9)

86.8 (1.3)

6.2 (1.2)

21.0 (1.8)

Baselinea

51.2 (0.3)

47.0 (0.5)

40.7 (0.6)

60.2 (0.5)

33.0 (0.4)

56.0 (0.5)

37.0 (0.5)

48.0 (0.5)

  1. aBaseline results are based on permutation test over the randomly shuffled labels (based on 100 repetitions of entire ‘learning with subsequent 10-fold CV evaluations’)