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Table 2 Evaluation results of each model using test set A

From: Using machine learning models to improve stroke risk level classification methods of China national stroke screening

Learning method

Precision (95% CI)

Recall (95% CI)

F1-score (95%CI)

AUC (95% CI)

Logistic regression

91.84% [91.81,91.87%]

97.82% [97.76,97.88%]

94.74% [94.69,94.78%]

99.14% [99.09,99.19%]

Naïve Bayesian

69.48% [69.42,69.54%]

97.35% [97.31,97.39%]

81.09% [81.03,81.14%]

98.44% [98.42,98.46%]

Bayesian network

69.66% [69.62,69.70%]

97.55% [97.53,97.57%]

81.28% [81.24,81.31%]

98.41% [98.38,98.44%]

Decision tree(C4.5)

92.25% [92.21,92.29%]

99.83% [99.78,99.88%]

95.89% [95.85,95.94%]

99.92% [99.90,99.94%]

Neural network

92.19% [92.14,92.24%]

99.72% [99.68,99.76%]

95.81% [95.76,95.85%]

99.15% [99.11,99.19%]

Random forest

97.33% [97.30,97.36%]

98.44% [98.41,98.47%]

97.88% [97.85,97.91%]

99.94% [99.92,99.96%]

Bagging with C4.5 decision tree

92.25% [92.22,92.28%]

99.74% [99.71,99.77%]

95.85% [95.82,95.88%]

99.93% [99.92,99.94%]

Voting

94.34% [94.32,94.36%]

99.66% [99.63,99.69%]

96.93% [96.91,96.95%]

99.94% [99.92,99.96%]

Boosting with C4.5 decision tree

95.51% [95.48,95.54%]

99.92% [99.89,99.95%]

97.67% [97.64,97.70%]

99.94% [99.91,99.97%]

  1. *The bold in tables is the maximum value of that evaluation standard