# Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
## 2 Background Information
### The Proposed Method
In this study, a novel method called FC2 RBF has been proposed for feature construction, which aims to improve the machine learning model by incorporating additional

Figure 8: Recall results visualization. Figure 7: Precision results visualization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**FC2 RBF**} & \multicolumn{3}{c}{**Method**} \\  & **RBF** & **MLP BFGS** & **MLP PCA** & **DATASET** \\ \hline
**0.9371** & 0.8906 & 0.8231 & 0.6004 & Eye-tracking \\
0.7639 & **0.8076** & 0.5405 & 0.5545 & Heart rate \\
**0.7065** & 0.6905 & 0.5872 & 0.5598 & Game scores \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recall of the methods applied for the classification procedures. details or connections that may not be directly discernible in the original features. Feature construction involves generating new features by applying mathematical operations, transformations, or combinations to existing ones. The primary objective of this method is to enhance the model by introducing more details or connections that may take time to become apparent in the original features. The experimental results presented in Section 4.2 confirm that the optimized features generated by the FC2 RBF algorithm in the comparative experiments demonstrated that using this feature creation method outperformed other machine learning methods for predicting neurodevelopmentalment disorders. The use of the FC2 RBF algorithm on the SmartSpeech datasets showed significant results. In the eye-tracking dataset, the test error was reduced by up to 65% compared with the MLP BFGS algorithm, which was the next best-performing algorithm. In the heart rate dataset, the test error was reduced by 6.14% compared with the RBF neural network, the next best-performing algorithm. Similarly, in the game scores dataset, the test error was reduced by 6.78% compared with the RBF neural network, which was again the next best-performing algorithm. Moreover, the proposed technique achieves high success rates using only two artificial features, which are generated as non-linear combinations of the original features in each dataset. Also, the proposed method was applied without any modification in the different SmartSpeech datasets. The number of learning model parameters is directly related to the dimensionality of the input problem, which is determined by the number of features. Therefore, complex problems, such as the evaluation and screening of neurodevelopmentalment disorders, require substantial memory resources to accommodate and handle the learning models. Moreover, as the number of parameters within computational models increases, it takes more time to modify those parameters. With a higher dimensionality of the data, a larger number of samples (patterns) is needed to achieve high learning rates. In this study, we managed to reduce the error rate and the dimensionality of the screening NDs features; for instance, in the game scores dataset, from 24 original features to two new artificial features produced from the original ones from non-linear transformations. Hence, only two features are required to obtain a low error rate in each dataset. Based on the results, the proposed technique performs better than the compared ones. Thus, it is faster, more accurate, and has higher sensitivity and specificity, especially for the eye-tracking dataset. The study's findings demonstrate the method's effectiveness with outstanding results in analyzing the SmartSpeech eye-tracking dataset, exhibiting lower error rates and, thus, higher accuracy, together with higher precision and sensitivity. This method can be integrated into the SmartSpeech machine learning model to support automated prediction in neurodevelopmental disorders (NDs), and to further assist clinicians in distinguishing children with NDs from those without during screening procedures. Future research may focus on addressing challenges and exploring innovations to enhance the efficiency, interpretability, and generalization capabilities of models addressing real-world challenges in NDs.