# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
## 3 Materials and Methods
### Data Analysis Using Machine Learning
#### 3.2.2 Enhanced Neural Network Model  
We changed the parameters to enhance the model, since the initial configuration only included psychometric. We reduced the number of training periods (number of epoch), loss function type, and batch size. We changed the model by using BinaryCrossentropy loss function, activation function ReLU, and Sigmoid and dropout of 10% between layers. The latter is a Dense feature layer with numerical columns to extract information from the input data. This feature layer allows the network to learn directly from those characteristics and propagate the features extracted from all the other layers [50]. The neural network architecture involves one input layer with the number of exploratory variables to be entered into the model, at least one hidden layer with 64 or 256 neurons per layer, and an output layer with only one neuron for binary classification (0 Control and 1 FASD). Our initial estimation used a simple approach of splitting the data into three chunks: train, validation, and test sets. However, this technique does not allow us to evaluate the performance thoroughly, especially when the amount of data is not enough (roughly less than 100). We used a K-fold cross-validation technique with K = 10 to corroborate the model's performance on data not used during training (Table 3). First, we improved the psychometric-based model by including a training period of 50 epochs with four hidden layers with 64 and 128 neurons per layer and a batch size of 32 (see Appendix A). Following this enhancement, we created a new model based on eye movement tests, including data regarding sex and age, to analyze the influence of these characteristics on the likelihood to predict FASD. We used the same model configuration and replaced the data with the antisaccades and memory-guided saccadic tests. In the prosaccade test data, the model was trained using 100 epochs with only two hidden layers with 64 and 128 neurons. All the models mentioned before used the activation function rectified linear unit (ReLU) and Sigmoid [51]. DTI was a particular case because we do not have enough data. Therefore, we used two hidden layers with 128 neurons, activation function Leaky ReLu, and training for 200 epochs, in this particular case, and a batch size of 16. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Number of Neurons**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**K-Fold Cross-Validation**} \\ \hline
**IL a** & **1 HL a** & **2 HL a** & **PT b** & **PV b** & **Avg (Accuracy)** & **Avg (Loss)** \\ \hline
20 & 15 & - & 80.72\% & 57.00\% & 71.22\% (\(\leftarrow\) 11.23) & 1.07 \\
25 & 15 & - & 80.72\% & 55.00\% & 72.69\% (\(\leftarrow\) 12.22) & 0.92 \\
25 & 20 & - & 90.24\% & 75.55\% & 77.37\% (\(\leftarrow\) 11.09) & 0.98 \\
25 & 30 & - & 85.00\% & 65.63\% & 69.87\% (\(\leftarrow\) 9.19) & 1.68 \\
25 & 20 & 15 & 88.00\% & 65.63\% & 72.82\% (\(\leftarrow\) 10.01) & 1.09 \\
50 & 15 & - & 93.98\% & 60.00\% & 66.54\% (\(\leftarrow\) 14.99) & 1.97 \\
100 & 50 & 25 & 91.00\% & 55.00\% & 73.65\% (\(\leftarrow\) 10.39) & 2.21 \\
200 & 15 & - & 97.59\% & 64.00\% & 69.04\% (\(\leftarrow\) 11.29) & 1.79 \\
200 & 50 & 50 & 97.00\% & 64.00\% & 69.74\% (\(\leftarrow\) 8.79) & 2.78 \\ \hline \hline \end{tabular} a (IL) Input Layer. (HL) Hidden Layer. b (PT) Accuracy in training. (PV) Accuracy in test (unobserved data).  
\end{table} 
Table 2: Network configuration for performance evaluation with psychometric data. The model's prediction power depends on the data using for training and testing. Therefore, we shuffle the dataset once splitting the dataset into K folds to avoid any issues with the learning process. Exploratory analysis shows that the data is imbalanced in prosecades, antisaccades, and memory-guided saccades. This issue may lead to unexpected mistakes and severe consequences in the data analysis, primarily to predict children with FASD.