Table 3 Classification accuracy of \(\text {NeurDNet}\) when only the first-visit tremor assessments are included in the test set.

From: A deep explainable artificial intelligent framework for neurological disorders discrimination

Classifier

Binary features

Probabilistic features

25%

35%

45%

55%

65%

75%

25%

35%

45%

55%

65%

75%

RF (entropy)

87.31

85.30

83.66

81.90

81.43

79.60

86.78

86.13

84.78

82.36

81.53

81.05

RF (gini)

87.59

85.80

83.50

82.03

80.96

79.77

86.66

85.63

84.83

82.23

81.38

80.83

SVM (rbf)

87.05

85.89

84.51

82.07

81.45

78.66

88.26

86.50

86.13

82.81

82.22

79.85

SVM (linear)

85.85

82.56

82.47

81.15

79.81

77.90

86.82

84.86

83.83

82.39

81.34

80.14

NB

84.99

83.93

79.95

81.65

77.07

75.61

87.60

86.44

85.11

84.54

82.57

81.09

LR

87.43

85.26

84.05

81.88

80.92

78.78

88.08

86.62

86.30

83.52

82.74

80.94

AdaBoost

86.26

82.53

82.70

80.21

77.69

76.46

85.79

83.59

82.32

80.78

78.63

75.06

LDA (svd)

81.13

78.10

76.10

70.13

67.04

67.82

79.12

77.02

76.49

71.58

66.14

62.99

LDA (lsqr)

81.13

78.10

76.04

70.13

62.49

49.41

79.12

77.02

76.49

71.58

66.14

51.06

QDA

79.18

80.77

77.65

70.20

62.15

60.04

93.05

89.66

77.59

71.63

59.92

54.01

DT (entropy)

80.85

79.04

78.25

76.60

76.42

74.96

79.76

79.14

78.44

77.39

75.61

73.82

DT (gini)

81.78

80.40

78.63

76.86

75.54

73.97

80.35

77.90

78.09

77.47

76.28

74.15

MLP (10)

85.41

83.33

82.54

78.90

78.09

77.60

83.31

81.84

81.50

79.72

78.15

77.83

MLP (30)

85.74

82.76

81.48

79.00

78.42

77.23

83.80

82.24

81.87

79.70

78.22

77.55

  1. The classification accuracy is measured across different choices of second-stage classifier, including random forests (RF), support vector machines (SVM), Naive Bayes Classifier(NB), logistic regression (LR), AdaBoost classifier (AB), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), decision trees (DT), and multi layer perceptron (MLP).