## 2 Materials and Methods
### Performance Estimation
GenClass is superior for the classes Disorder, ID, SLD, and CD, whereas DNN-4 is better for ASD and ADHD. For the aggregate class Disorder, GenClass has the highest observed accuracy of 92.83%. This finding may be utilized for automated screening to discriminate whether an individual has NDs. * The RBF method is the most accurate in the heart rate dataset, with an accuracy of at least 80.05%. It is notable that it achieves the best performance for all the classes under study. * As for the game-based dataset, the GenClass method has the highest accuracy for the classes Disorder, ASD, ID, and CD. The classes ADHD and SLD are better identified using the RBF algorithm. However, in most other cases GenClass and DNN-4 outperform the rest. It is worth noting that GenClass is expected to have longer execution times since it is based on genetic algorithms. Nevertheless, in this study we have employed the parallelization feature of the software GenClass [91] to speed up the process. Similar research attempts to identify NDs have been reported in the literature. For example, one such study evaluated the ability of drag-and-drop data to be used to classify children with developmental disabilities [12]. Data were collected from 223 children with typical development and 147 children with developmental disabilities via a mobile application (DoBrain). A deep learning CNN algorithm was developed to classify an area under the curve (AUC) of 0.817. Furthermore, in line with our study, a binary classifier has also been trained using paralinguistic features extracted from typically developing children and children suffering from Speech Sound Disorders (SSD), reporting 87% accuracy [60]. In the same direction as our study, the HRV was also used as a biomarker to distinguish autistic and typical children by applying several machine learning algorithms, that is, the Logistic Regression, Linear Discriminant Analysis, and Cubic Support Vector Machine [39]. Logistic Regression proved to be the best classifier for a color stimulus test in that study, whereas Linear Discriminant Analysis was better in the baseline test. Moreover, an important biomarker to detect ASD can be considered similar to our research which focused on eye tracking data [15]. While finding the best method to predict autism with the help of eye tracking scan path images, the DNN classifier was compared to traditional machine learning approaches such as Boosted Decision Tree, Deep Support Vector Machine, and Decision Jungle. The DNN model outperformed the other machine learning techniques with an AUC of 97%, sensitivity of 93.28%, specificity of 91.38%, negative prediction value (NPV) of 94.46%, and positive predictive value (PPV) of 90.06% [15]. Moreover, RBF also reported reliable results in a study with an attempt to identify children with ID that was done using two different feature extraction methods of speech samples, that is, the Linear Predictive Coding based cepstral parameters and Mel-frequency cepstral coefficients, along with four classifiers, that is, k-nearest neighbor, support vector machine, linear discriminant analysis, and RBF neural network [92]. The RBF classification model was the best technique for classifying disordered speech, giving higher accuracy compared to the rest of the classifiers (>90%). Furthermore, this study's sample size is analogous to other research [12, 15, 93] due to the high costs of collecting the data involving human subjects and the ongoing development of tasks and experimental techniques that can discriminate between various situations to the greatest extent possible. Similar to prior studies [93], in this study, experimenting while collecting a single multi-dimensional data sample may take 1.5 to 4 h of participant's time (such as setting up, testing, and setting down) and 2 to 6 h of participant time (which encompasses travel time). Furthermore, reaching out to people and encouraging participation is complex, making recruiting many participants with NDs difficult.