Table 4 Random Forest performance validation for predicting whether MCI subjects will progress to AD or not (tenfold cross-validation; Second Layer).

From: A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Modalities used

Precision (%)

Recall (%)

Accuracy (%)

F1-score (%)

AUC

All

87.12 ± 1.52

81.31 ± 2.00

84.18 ± 1.77

83.21 ± 2.43

84.17 ± 1.99

CFA

82.14 ± 1.40

84.19 ± 1.90

82.16 ± 1.60

83.15 ± 1.50

82.15 ± 1.50

MRI

75.23 ± 1.88

72.25 ± 1.59

71.18 ± 2.01

72.17 ± 1.92

71.18 ± 1.89

PET

68.22 ± 2.22

68.53 ± 1.98

68.25 ± 1.99

66.39 ± 1.99

68.24 ± 2.01

Genetics

73.11 ± 1.73

68.36 ± 1.72

70.14 ± 1.79

69.24 ± 1.89

70.13 ± 1.80

MH

58.16 ± 4.60

52.22 ± 4.90

55.15 ± 3.73

54.19 ± 3.76

55.15 ± 3.76

CFA + MRI

83.11 ± 2.31

84.22 ± 2.20

83.14 ± 2.51

83.15 ± 2.26

83.14 ± 2.22

CFA + PET

85.17 ± 1.70

84.29 ± 2.90

84.19 ± 1.90

84.21 ± 2.10

84.19 ± 1.90

CFA + Genetics

82.14 ± 1.91

81.27 ± 1.98

81.16 ± 1.93

81.17 ± 1.96

81.16 ± 1.95

CFA + MH

84.16 ± 3.77

82.23 ± 4.60

82.17 ± 3.80

83.18 ± 3.33

82.17 ± 3.80

CFA + PET + MRI

86.09 ± 2.10

84.23 ± 2.30

84.11 ± 2.15

85.14 ± 2.22

85.11 ± 2.15

CFA + PET + Genetics

90.11 ± 1.50

83.21 ± 2.21

86.08 ± 1.04

85.11 ± 2.00

86.08 ± 1.05

CFA + PET + MH

86.09 ± 3.45

84.17 ± 4.20

84.08 ± 3.71

85.09 ± 4.01

84.08 ± 3.72

CFA + PET + Genetics + MRI*

88.07 ± 0.70

86.13 ± 1.30

87.08 ± 0.80

87.09 ± 0.90

87.08 ± 0.80

CFA + PET + Genetics + MH

86.09 ± 3.51

86.13 ± 4.70

86.08 ± 3.32

86.08 ± 3.99

86.08 ± 3.36

  1. BA: Balanced accuracy. Asterisk ( ): is the subset of features with the best predictive performance. Performance: Mean ± standard deviation.