Table 4 Evaluation results of each model using screening data in 2016
Learning method | Precision (95% CI) | Recall (95% CI) | F1-score (95%CI) | AUC (95% CI) |
|---|---|---|---|---|
Logistic regression | 90.56% [90.52,90.60%] | 96.35% [96.31,96.39%] | 93.37% [93.33,93.41%] | 97.96% [99.09,99.19%] |
Naïve Bayesian | 66.96% [66.93,66.99%] | 94.99% [94.95,95.03%] | 78.55% [78.51,78.58%] | 96.64% [96.62,96.66%] |
Bayesian network | 67.50% [67.47,67.53%] | 93.85% [93.80,93.90%] | 78.52% [78.49,78.56%] | 96.86% [96.82,96.90%] |
Decision tree(C4.5) | 91.95% [91.90,92.00%] | 98.12% [98.09,98.15%] | 94.93% [94.89,94.98%] | 99.36% [99.33,99.39%] |
Neural network | 91.82% [91.78,91.86%] | 98.52% [98.49,98.55%] | 95.05% [95.02,95.09%] | 99.23% [99.20,99.26%] |
Random forest | 96.89% [96.86,96.92%] | 95.76% [95.74,95.78%] | 96.32% [96.30,96.35%] | 99.41% [99.39,99.43%] |
Bagging with C4.5 decision tree | 92.21% [92.19,92.23%] | 98.86% [98.83,98.89%] | 95.42% [95.39,95.44%] | 99.39% [99.92,99.94%] |
Voting | 92.12% [92.07,92.17%] | 98.98% [98.96,99.00%] | 95.43% [95.39,95.46%] | 99.39% [99.36,99.42%] |
Boosting with C4.5 decision tree | 94.89% [94.85,94.93%] | 99.12% [99.09,99.15%] | 96.96% [96.92,96.99%] | 99.41% [99.38,99.44%] |