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Table 3 10-fold cross-validation results for the binary classification models for each imputed data set, working with the two classes nonfrail and frail

From: Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Prediction method

Accuracy

AUC

Sensitivity

Specificity

Precision

F1-Score

Imputation 1

      

Naive Bayes

73.20 ± 5.97%

0.756 ± 0.052

0.656 ± 0.102

0.856 ± 0.079

0.885 ± 0.054

0.749 ± 0.067

CART

72.77 ± 5.20%

0.710 ± 0.061

0.782 ± 0.108

0.639 ± 0.168

0.789 ± 0.065

0.778 ± 0.049

Bagging CART

75.51 ± 7.16%

0.731 ± 0.070

0.830 ± 0.086

0.633 ± 0.084

0.786 ± 0.048

0.806 ± 0.060

C5.0

77.83 ± 7.13%

0.752 ± 0.086

0.860 ± 0.056

0.644 ± 0.164

0.804 ± 0.075

0.829 ± 0.051

Random forest

77.64 ± 5.62%

0.755 ± 0.053

0.844 ± 0.089

0.667 ± 0.087

0.806 ± 0.041

0.823 ± 0.050

Support vector machines (RBF)

77.64 ± 6.55%

0.762 ± 0.065

0.824 ± 0.09

0.700 ± 0.099

0.819 ± 0.053

0.819 ± 0.057

Linear discriminant analysis

75.11 ± 5.34%

0.739 ± 0.042

0.789 ± 0.096

0.689 ± 0.047

0.805 ± 0.023

0.795 ± 0.055

Imputation 2

      

Naive Bayes

72.78 ± 6.47%

0.750 ± 0.059

0.656 ± 0.109

0.844 ± 0.094

0.878 ± 0.063

0.745 ± 0.072

CART

70.89 ± 5.94%

0.699 ± 0.057

0.741 ± 0.098

0.656 ± 0.104

0.781 ± 0.047

0.757 ± 0.058

Bagging CART

75.11 ± 6.59%

0.729 ± 0.072

0.820 ± 0.089

0.639 ± 0.134

0.792 ± 0.066

0.802 ± 0.054

C5.0

77.39 ± 7.35%

0.745 ± 0.093

0.867 ± 0.057

0.622 ± 0.192

0.797 ± 0.082

0.828 ± 0.050

Random forest

77.01 ± 6.65%

0.752 ± 0.064

0.827 ± 0.101

0.678 ± 0.101

0.809 ± 0.052

0.815 ± 0.060

Support vector machines (RBF)

77.63 ± 7.01%

0.761 ± 0.071

0.827 ± 0.085

0.694 ± 0.102

0.816 ± 0.057

0.820 ± 0.060

Linear discriminant analysis

76.14 ± 5.15%

0.752 ± 0.046

0.792 ± 0.081

0.711 ± 0.057

0.817 ± 0.032

0.803 ± 0.050

Imputation 3

      

Naive Bayes

73.41 ± 5.64%

0.757 ± 0.057

0.664 ± 0.083

0.849 ± 0.102

0.885 ± 0.069

0.755 ± 0.056

CART

73.21 ± 5.75%

0.728 ± 0.07

0.746 ± 0.064

0.709 ± 0.14

0.815 ± 0.067

0.776 ± 0.045

Bagging CART

78.28 ± 3.92%

0.764 ± 0.057

0.841 ± 0.058

0.688 ± 0.148

0.823 ± 0.062

0.828 ± 0.026

C5.0

74.06 ± 7.12%

0.709 ± 0.089

0.837 ± 0.057

0.581 ± 0.181

0.774 ± 0.073

0.802 ± 0.048

Random forest

77.62 ± 6.65%

0.762 ± 0.076

0.820 ± 0.068

0.704 ± 0.134

0.824 ± 0.068

0.820 ± 0.052

Support vector machines (RBF)

79.32 ± 5.00%

0.779 ± 0.056

0.838 ± 0.049

0.720 ± 0.09

0.833 ± 0.048

0.834 ± 0.040

Linear discriminant analysis

78.47 ± 4.77%

0.773 ± 0.051

0.821 ± 0.059

0.726 ± 0.085

0.833 ± 0.045

0.825 ± 0.040

Imputation 4

      

Naive Bayes

72.78 ± 5.89%

0.750 ± 0.061

0.657 ± 0.083

0.843 ± 0.111

0.881 ± 0.075

0.749 ± 0.057

CART

71.26 ± 5.83%

0.697 ± 0.053

0.762 ± 0.095

0.631 ± 0.083

0.774 ± 0.043

0.765 ± 0.058

Bagging CART

76.38 ± 5.77%

0.747 ± 0.069

0.817 ± 0.076

0.676 ± 0.147

0.812 ± 0.065

0.811 ± 0.046

C5.0

74.25 ± 7.13%

0.712 ± 0.085

0.837 ± 0.057

0.587 ± 0.157

0.774 ± 0.07

0.803 ± 0.052

Random forest

76.99 ± 5.90%

0.755 ± 0.069

0.817 ± 0.069

0.693 ± 0.136

0.819 ± 0.067

0.815 ± 0.046

Support vector machines (RBF)

78.47 ± 5.14%

0.771 ± 0.057

0.827 ± 0.053

0.714 ± 0.092

0.829 ± 0.049

0.827 ± 0.041

Linear discriminant analysis

78.06 ± 5.39%

0.772 ± 0.057

0.807 ± 0.061

0.737 ± 0.091

0.837 ± 0.049

0.820 ± 0.045

Imputation 5

      

Naive Bayes

73.41 ± 5.45%

0.756 ± 0.053

0.664 ± 0.088

0.849 ± 0.098

0.885 ± 0.066

0.754 ± 0.057

CART

71.67 ± 7.79%

0.702 ± 0.087

0.762 ± 0.100

0.642 ± 0.166

0.786 ± 0.089

0.769 ± 0.066

Bagging CART

76.79 ± 4.69%

0.749 ± 0.053

0.827 ± 0.071

0.671 ± 0.115

0.809 ± 0.049

0.815 ± 0.039

C5.0

75.31 ± 4.08%

0.726 ± 0.055

0.837 ± 0.065

0.615 ± 0.138

0.787 ± 0.055

0.808 ± 0.030

Random forest

78.03 ± 5.10%

0.764 ± 0.060

0.830 ± 0.073

0.698 ± 0.129

0.824 ± 0.061

0.824 ± 0.041

Support vector machines (RBF)

78.47 ± 5.39%

0.771 ± 0.059

0.827 ± 0.055

0.714 ± 0.092

0.828 ± 0.049

0.827 ± 0.043

Linear discriminant analysis

77.62 ± 5.35%

0.769 ± 0.058

0.800 ± 0.063

0.737 ± 0.102

0.836 ± 0.054

0.816 ± 0.045

  1. The highest obtained value for each performance category for each imputed data set is marked in bold