Table 4.
Performance comparison of machine learning algorithms in each dataset of CNUH data.
| Model | Measure | Dataset | ||
|---|---|---|---|---|
| Lab | ISNCSCI | Lab + ISNCSCI | ||
| GNN-GCN | Sensitivity | 0.180 ± 0.195 | 0.426 ± 0.218 | 0.362 ± 0.200 |
| Specificity | 0.923 ± 0.036 | 0.947 ± 0.035 | 0.945 ± 0.037 | |
| Accuracy | 0.877 ± 0.034 | 0.914 ± 0.034 | 0.908 ± 0.033 | |
| AUC | 0.551 ± 0.096 | 0.686 ± 0.107 | 0.653 ± 0.097 | |
| F1-score | 0.538 ± 0.078 | 0.666 ± 0.092 | 0.635 ± 0.087 | |
| DNN | Sensitivity | 0.108 ± 0.134 | 0.398 ± 0.214 | 0.388 ± 0.194 |
| Specificity | 0.944 ± 0.031 | 0.947 ± 0.036 | 0.949 ± 0.033 | |
| Accuracy | 0.892 ± 0.031 | 0.913 ± 0.035 | 0.914 ± 0.028 | |
| AUC | 0.526 ± 0.068 | 0.672 ± 0.105 | 0.669 ± 0.092 | |
| F1-score | 0.523 ± 0.066 | 0.654 ± 0.099 | 0.654 ± 0.077 | |
| SVM_linear | Sensitivity | 0.124 ± 0.132 | 0.416 ± 0.204 | 0.418 ± 0.181 |
| Specificity | 0.961 ± 0.035 | 0.950 ± 0.027 | 0.925 ± 0.038 | |
| Accuracy | 0.909 ± 0.035 | 0.889 ± 0.032 | 0.894 ± 0.036 | |
| AUC | 0.543 ± 0.069 | 0.643 ± 0.072 | 0.672 ± 0.089 | |
| F1-score | 0.547 ± 0.084 | 0.614 ± 0.065 | 0.636 ± 0.081 | |
| SVM_RBF | Sensitivity | 0.140 ± 0.173 | 0.362 ± 0.153 | 0.420 ± 0.214 |
| Specificity | 0.944 ± 0.028 | 0.924 ± 0.035 | 0.957 ± 0.028 | |
| Accuracy | 0.894 ± 0.028 | 0.917 ± 0.025 | 0.924 ± 0.025 | |
| AUC | 0.542 ± 0.086 | 0.683 ± 0.098 | 0.689 ± 0.103 | |
| F1-score | 0.537 ± 0.083 | 0.661 ± 0.086 | 0.676 ± 0.085 | |
| KNN | Sensitivity | 0.340 ± 0.175 | 0.562 ± 0.236 | 0.538 ± 0.223 |
| Specificity | 0.875 ± 0.040 | 0.913 ± 0.030 | 0.910 ± 0.029 | |
| Accuracy | 0.842 ± 0.037 | 0.891 ± 0.027 | 0.887 ± 0.028 | |
| AUC | 0.607 ± 0.085 | 0.737 ± 0.114 | 0.724 ± 0.109 | |
| F1-score | 0.559 ± 0.053 | 0.661 ± 0.068 | 0.651 ± 0.069 | |
| Random Forest | Sensitivity | 0.066 ± 0.114 | 0.386 ± 0.191 | 0.378 ± 0.194 |
| Specificity | 0.996 ± 0.008 | 0.943 ± 0.031 | 0.945 ± 0.033 | |
| Accuracy | 0.938 ± 0.010 | 0.909 ± 0.028 | 0.910 ± 0.033 | |
| AUC | 0.531 ± 0.056 | 0.665 ± 0.093 | 0.662 ± 0.101 | |
| F1-score | 0.533 ± 0.083 | 0.646 ± 0.080 | 0.649 ± 0.093 | |
| Logistic Regression | Sensitivity | 0.148 ± 0.160 | 0.408 ± 0.184 | 0.442 ± 0.191 |
| Specificity | 0.941 ± 0.035 | 0.926 ± 0.033 | 0.928 ± 0.033 | |
| Accuracy | 0.892 ± 0.032 | 0.895 ± 0.032 | 0.898 ± 0.032 | |
| AUC | 0.545 ± 0.078 | 0.667 ± 0.093 | 0.685 ± 0.094 | |
| F1-score | 0.538 ± 0.070 | 0.636 ± 0.083 | 0.648 ± 0.082 | |
The KNN model trained with the “ISNCSCI” dataset from CNUH demonstrated the highest performance, with an AUC of 0.737. Abbreviations: Lab = laboratory data; ISNCSCI = International Standards for Neurological Classification of Spinal Cord Injury.