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Table 5 Results of PCA analgesic consumption (PCA only) prediction

From: Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

PCA Analgesic Consum. Prediction (%)

C4.5 bagging

C4.5 AdaBoost

C4.5

ANN*

Random Forest

Rotation Forest

SVM

NB

Low Consum. Sensitivity

84.3

79.0

76.9

89.2

95.4

84.1

99.9

81.4

Med Consum. Sensitivity

65.8

54.3

51.5

19.1

47.2

60.6

0.0

48.1

High Consum. Sensitivity

47.5

45.1

45.4

8.4

31.8

51.0

0.0

50.8

Low Consum. Precision

81.7

77.4

76.1

62.0

73.2

80.5

52.8

75.4

Med Consum. Precision

60.7

53.6

51.2

20.7

61.4

59.7

0.0

55.6

High Consum. Precision

75.1

52.3

49.3

22.8

85.7

68.2

0.0

51.2

Overall Accuracy

73.1

66.1

64.1

54.7

70.6

71.7

52.7

65.4

  1. *ANN consisting of an input layer of 279 input units, one hidden layer of 140 hidden units, and one output layer of 3 output units.
  2. Learning rate= 0.3; momentum rate= 0.2.
  3. SVM using a radial basis function, exp(−gamma*|u-v|2), where gamma=1/(number of attributes)=1/279.