In the original article, there was a mistake in “TABLE 6 | Binary classification results” as published. We made errors while recording the supporting result values of sensitivity, specificity, F1-score, and precision. However, the main results of accuracy remain intact. To ensure the correctness and reproducibility of the results, we calculated all of these measures again. In addition, sensitivity, and recall represent the same measure, therefore, we omit the recall results. The corrected “TABLE 6 | Binary classification results” appears below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.
Table 6.
Binary classification results.
| Classifier | Group | |||
|---|---|---|---|---|
| ADHDC-TDC | ADHDC-ADHDI | ADHDI-TDC | ||
| ELM | Accuracy (%) | 89.286 | 85.714 | 92.857 |
| p-value | <0.0001 | <0.0001 | <0.0001 | |
| Sensitivity | 86.667 | 77.778 | 100.00 | |
| Specificity | 92.310 | 100.00 | 87.500 | |
| F1-Score | 89.655 | 87.500 | 92.307 | |
| Precision | 92.857 | 100.00 | 85.714 | |
| ELM-NFS | Accuracy (%) | 71.429 | 67.857 | 67.857 |
| p-value | <0.0351 | <0.0348 | <0.0343 | |
| Sensitivity | 100.00 | 77.780 | 69.231 | |
| Specificity | 63.641 | 63.160 | 66.667 | |
| F1-Score | 60.000 | 60.870 | 66.667 | |
| Precision | 42.857 | 50.000 | 64.290 | |
| SVM linear | Accuracy (%) | 71.429 | 82.143 | 67.857 |
| Sensitivity | 75.000 | 76.471 | 61.900 | |
| Specificity | 68.750 | 90.910 | 85.714 | |
| F1-Score | 69.231 | 83.869 | 74.290 | |
| Precision | 64.286 | 92.857 | 92.857 | |
| SVM-RBF | Accuracy (%) | 53.571 | 57.143 | 60.714 |
| Sensitivity | 53.333 | 55.556 | 66.667 | |
| Specificity | 53.850 | 60.000 | 57.894 | |
| F1-Score | 55.170 | 62.500 | 52.170 | |
| Precision | 57.140 | 71.429 | 42.860 | |
ELM, extreme learning machine; TDC, typically developing children; ADHDI, attention deficit/hyperactivity disorder-inattentive type; ADHDC, attention-deficit/hyperactivity disorder combined type; SVM, support vector machine; RBF, radial basis function; NFS, no feature selection applied. Besides the ELM-NFS all the three (ELM, SVM linear, and SVM-RBF) based classification scores were obtained with the most discriminative features selected through the hierarchical feature selection method. Bold values represents the highest accuracy and its corresponding evaluation measures.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.