# Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
## 2 Background Information
### The Proposed Method
Most prediction models place the data points that they use in one of these four categories:

1. True positive (TP)--the individual in question does in fact have NDs and our prediction was accurate that the individual does have NDs. 2. True negative (TN)--the individual in question does not in fact have NDs and our prediction was accurate that the individual does not have NDs. 3. False positive (FP)--although the individual in question does not in fact have NDs, our prediction was inaccurate that the individual does have NDs. The term for this kind of error is a Type 1 error. 4. False negative (FN)--although the individual in question does in fact have NDs, our prediction was inaccurate that the individual does not have NDs. The term for this kind of error is a Type 2 error. For the classification of the datasets, the reported error is the average classification error, as measured in the test set. The classification error refers to the percentage of patterns in the test set that were assigned to a class that was not the anticipated one. Error rate is calculated by Equation (1):

\[\text{Error rate}=\frac{\text{FP}+\text{FN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]

The precision metric quantifies the degree of accuracy of our positive predictions, meaning that it indicates the proportion of projected positive points that really occurred. Equation (2) defines precision:

\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]

The recall metric quantifies the proportion of positive instances that our model successfully recognized. In other words, it assesses the accuracy of our model in correctly classifying positive instances out of the total number of instances classified as positive. Recall and sensitivity are synonymous. Next, Equation (3) specifies recall:

\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]Figure 5: Overall flowchart and study structure (g* stands for the best chromosome of the population). ### Experimental Results

Table 3 shows the results of the classification experiments for each of the methods in use in terms of error rate percentage. The experiments took place separately for each of the three datasets. Figure 6 presents a visualization of the error rates results. The precision results of the classification studies for each of the employed methods are presented in Table 4. The experiments were conducted individually for each of the three datasets. The results indicate that the FC2 RBF method bested the other three approaches in terms of precision, with great performance on the eye-tracking dataset. A visualization of the precision results is depicted in Figure 7.

The recall findings for each of the methods utilized in the classification experiments are presented in Table 5, designated as sensitivity. The experiments were conducted separately for each of the three datasets. Figure 8 shows a visualization of the recall findings. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{**Method**} \\
**FC2 RBF** & **RBF** & **MLP BFGS** & **MLP PCA** & **DATASET** \\ \hline
**0.9125** & 0.6887 & 0.7644 & 0.5558 & Eye-tracking \\
**0.5748** & 0.5264 & 0.5067 & 0.5006 & Heart rate \\
**0.5604** & 0.5344 & 0.5574 & 0.5344 & Game scores \\ \hline \end{tabular}
\end{table} 
Table 4: Precision of the methods applied for the classification procedures. Figure 6: Error rate results visualization. ## 5 Discussion--Conclusions

This study contributes to identifying the most accurate and efficient algorithms for a practical application such as SmartSpeech by proposing and comparing ML methodologies on these particular, consistent datasets. It offers valuable analysis of the advantages and disadvantages of various algorithms, enabling informed decision-making in the development and implementation of the SmartSpeech ML solution that may serve as a valuable screening tool for clinicians and other specialists to identify NDs from non-NDs children and, thus, plays a crucial role in their rehabilitation strategy.