Table 2. Test performance of various lineage typing ML models.
| ST | Model | Selected peaks | Acc | Sen | Spe |
|---|---|---|---|---|---|
| ST5 | QC | 8 | 0.94 ± 0.04 | 0.94 ± 0.05 | 0.95 ± 0.05 |
| SNN | 24 | 0.63 ± 0.05 | 0.91 ± 0.05 | 0.34 ± 0.04 | |
| GA_KNN1 | 30 | 0.83 ± 0.03 | 0.68 ± 0.03 | 0.98 ± 0.05 | |
| GA_KNN3 | 30 | 0.84 ± 0.03 | 0.71 ± 0.03 | 0.97 ± 0.02 | |
| GA_KNN5 | 19 | 0.83 ± 0.03 | 0.68 ± 0.03 | 0.98 ± 0.02 | |
| GA_KNN7 | 14 | 0.88 ± 0.03 | 0.79 ± 0.03 | 0.97 ± 0.02 | |
| ST59 | QC | 22 | 0.76 ± 0.03 | 0.85 ± 0.04 | 0.67 ± 0.04 |
| SNN | 5 | 0.68 ± 0.04 | 0.57 ± 0.05 | 0.79 ± 0.04 | |
| GA_KNN1 | 30 | 0.78 ± 0.03 | 0.62 ± 0.04 | 0.94 ± 0.03 | |
| GA_KNN3 | 29 | 0.82 ± 0.02 | 0.70 ± 0.02 | 0.94 ± 0.03 | |
| GA_KNN5 | 29 | 0.85 ± 0.03 | 0.74 ± 0.03 | 0.96 ± 0.02 | |
| GA_KNN7 | 22 | 0.82 ± 0.02 | 0.67 ± 0.02 | 0.97 ± 0.02 | |
| ST239 | QC | 6 | 0.81 ± 0.02 | 0.91 ± 0.01 | 0.72 ± 0.02 |
| SNN | 1 | 0.58 ± 0.03 | 0.72 ± 0.03 | 0.44 ± 0.04 | |
| GA_KNN1 | 30 | 0.80 ± 0.02 | 0.88 ± 0.03 | 0.72 ± 0.02 | |
| GA_KNN3 | 30 | 0.80 ± 0.02 | 0.85 ± 0.02 | 0.74 ± 0.02 | |
| GA_KNN5 | 29 | 0.81 ± 0.02 | 0.89 ± 0.01 | 0.72 ± 0.02 | |
| GA_KNN7 | 28 | 0.83 ± 0.02 | 0.90 ± 0.02 | 0.76 ± 0.02 |
Notes:
QC, QuickClassifier; SNN, supervised neural network; GA, genetic algorithm; KNN, K-Nearest Neighbor.
The number following “GA-KNN” indicates the number of the nearest neighbors used in models.
Selected peaks: number of peaks selected by the models; Acc, accuracy; Sen, sensitivity; Spe, specificity. Performance metrics are expressed as mean ± standard error.