# Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
## 2 Background Information
### The Proposed Method
The variables obtained by the software are relevant, with the fixation being the fundamental metric for eye-tracking. A fixation is defined as a cluster of gaze points close to each other in space and time, which means the individual is looking at a specific region. Fixations are the most common measures of visual attention. During the game, certain areas on the screen are predetermined as areas of interest (AOI). An AOI may be, for example, a face, an animal, or a moving object, and it is defined as a rectangular region with specific coordinates and a specific time duration. The software extracted three fundamental variables from the eye movement experiments:

* Fixation count (FC), i.e., the total number of fixations in an AOI;
* Time to first fixation (TTFF), i.e., the time needed after an AOI is visible until the first fixation is counted on it;
* The total duration of fixations (TS), i.e., the total time that an individual spends looking at a specific AOI. The data collection of the eye-tracking software depends on how the subject reacts to stimuli. The subjects, especially children, move around a lot; thus, the camera fails to take some measurements. Therefore, due to missing values in our datasets, in the final variables selected after clearing the data, there were no variables with TTFF metrics. Eliminating missing values was necessary, and we needed to ensure that all cases were filled with valid data. After finishing the game, all data were exported and automatically stored as variables in a remote server. The abovementioned variables belong to 3 categories: the game variables (25), which were the scores from the game activities; the eye-tracking variables (16); and the heart rate variables (15) [3]. The data variables for the game score, eye-tracking, and heart rate datasets are illustrated in Figures 2-4, respectively, also presenting the dimensionality of each dataset. Table 1 illustrates in more detail the description of the variables for the game dataset. For the experiments, in total, 435 children aged \(8.8\pm 7.4\) years participated, of which 224 were boys and 211 were girls. The parents provided written consent along with the child's neurodevelopmental or medical history. After completion of all the games, data were gathered for each child that belong to the three aforementioned sources--game scores, eye-tracking, and heart rate--forming the three datasets in the study. The eye-tracking dataset had 309 cases, the heart rate dataset had 181 cases, and the game scores had 435 cases. The sample was further divided into two groups according to the existence of a specific neurodevelopmental disorder or not, inserting a new variable in the datasets with the name Disorder. This variable denotes whether an individual has one of the Diagnostic and Statistical Manual of Mental Disorders [1], which defines one or more of the following five disorders: Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Intellectual Disability (ID), Specific Learning Disorder (SLD), and Communication Disorder (CD). The Disorder variable is binary with values true/false, denoting two classes for the classification procedures that follow. Figure 4: Variables comprising the heart rate dataset. Figure 3: Variables comprising the eye-tracking dataset. Figure 2: Variables comprising the game scores dataset. ## 4 Experiments

### Experimental Datasets, Methods, and Parameter Details

This section reports on the assessment of the proposed FC2 RBF technique's efficacy in creating artificial features for feature learning and class prediction using the three datasets from the SmartSpeech project (see Section 3). These issues have been extensively examined by numerous scholars in the pertinent academic discourse, encompassing a diverse array of research domains spanning from economics to health [3, 40, 54, 55, 73, 74]. The parameters used in the employed algorithms are shown in Table 2. The following methods were used:

1. RBF--an RBF neural network with H processing nodes. 2.