## 2 Materials and Methods
### Performance Estimation  
The 10-fold cross-validation technique has consistently been employed as an evaluation method to fairly assess the predictive ability and produce its efficiency (Figure 5). We divided each dataset into ten partitions. Nine of the partitions we created were used for training, and the final partition was used for testing. For each instance we performed thirty independent experiments and calculated each algorithm and the average classification errors. Moreover, we used different seed numbers for every experiment by using the C programming language's drand48() random number generator. For the experiments, we  
Figure 4: GenClass flowchart. used freely downloadable software from [[https://github.com/itsoulos/IntervalGenetic](https://github.com/itsoulos/IntervalGenetic)]([https://github.com/itsoulos/IntervalGenetic](https://github.com/itsoulos/IntervalGenetic)), (accessed on 18 February 2023). For classification evaluation, a confusion matrix is used to calculate the error rate, precision, recall, and accuracy, presented below in Equations (1)-(4), respectively [39,49]:  
\[\text{Error rate}=\frac{\text{FP}+\text{FN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{4}\]  
Overall, to overview the methods followed in this study, Figure 6 visually demonstrates the study's workflow. Figure 5: Ten-fold cross-validation diagram. For the eye tracking dataset, it is shown that the best overall results are obtained with the GenClass method with a total average error rate of 12.03%. More specifically, this method is found to be more suitable for the Disorder class, which denotes whether a child has a disorder or not and particularly for the disorders ID, SLD, and CD, with average error rates of 13.07%, 11.12% and 11.30%, respectively. DNNs on the other hand are proved to be more accurate for distinguishing the NDs of ASD and ADHD, with average error rates of 13.67% and 12.56%, respectively. The number of layers seems to have a small impact on the outcome with the four-layer DNN achieving the highest performance. For the heart rate dataset, the RBF classifier is superior to the others for all the classes with an overall average error rate of 18.73%. This method is proved to be more appropriate when the biometric data consist of heart rate measurements, whereas the difference in performance against to the other classifiers is remarkable. For the game scores dataset, the GenClass classifier is found to be slightly better in detecting all the target disorder variables with an average error rate of 22.08%. Furthermore, Table 6 compares the precision and recall for the Disorder datasets. Finally, a comparison in terms of higher classification accuracies is shown in Table 7 for each class and SmartSpeech dataset. in Greek. Ten-fold cross-validation was chosen for evaluating model efficacy since it produces high variability in testing and training data, decreases bias, and delivers consistent findings for all tries, parameters, and models. The results of this research give a direct comparison of the different machine learning methods employed on this dataset, which are RBF, DNN, and GenClass. The reported results of this study (Tables 3-5) display the comparison of all the methods employing the performance metric of the error rate (%). Thus, a smaller value implies better performance. Precision and recall metrics are also displayed for the class Disorder (Table 6). Finally, the highest performance classification methods in accuracy metrics are reported for each class and dataset (Table 7). Particularly, Table 7 clearly illustrates the tendency of the specific methods to dominate in each dataset and class; more specifically:  
* For the eye tracking measurements, the GenClass and the DNN-4 have proven to be the best choices, with an accuracy of at least 86.33% for the ASD population.