# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
## 5 Discussion  
This paper evaluated the usage of computational algorithms based on ANN to classify children with FASD for an improved medical diagnosis. This paper estimated the prediction model using numerical psychometric data, DTI, and saccadic eye movement. Unlike most of the current research that implements ML using image-based data to predict FASD, the novelty of this paper is that it uses numerical data to estimate the model. Although [PERSON] et al. (2019) [15] collected and used the same data, our results show a better performance using ANN instead of SVMR or other methods. First, for psychometric data, a basic network configuration was obtained with correctness with test data of 75.5% with a Leaky-ReLU activation function, which did not require data normalization. Subsequently, a change was made in the configuration of the networks adding a feature layer. This layer allows identifying the essential characteristics for FASD diagnosis. This feature significantly contributes to the classification, achieving 88% accuracy. It also produced the better results using the K-Fold Cross validation (K = 10) and K-Fold Cross-Validation (K = 5), suggesting that this should be the direction for improving FASD diagnosis using ANN. For the rest of the data, a network configuration with a feature layer was used to evaluate the neural network model performance. Using numerical origin (Saccade, Prosaccade, and DTI) data related to FASD, models achieved an accuracy greater than 70%. Despite this higher rate obtained by all of these configurations, it is not yet considered to be a safe prediction for a medical diagnoses. The confusion matrices obtained demonstrate that the model predicts at least 71% of patients diagnosed with FASD, from the around 50% that this group represents. However, the prediction of control patients has over 77% of patients who have not been diagnosed with FASD (except Prosaccadic task). These matrices suggest that the model must be recalibrated or overlearned from the data of patients with FASD, or else some control patients may have FASD but have not been diagnosed. These results are not completely demonstrated; thus, the use of ANN as a diagnostic tool must be improved before its use can be suggested. Results show that the estimated models present a better performance in all cases compared to the results obtained by [PERSON]'s research. Overall, the use of ANN outperformed by 14.22% (S.D. 10.58) the results obtained by [PERSON]'s model. Unlike the model obtained by [PERSON], all the characteristics obtained from each study are included in the estimated model. This is a distinctive feature since [PERSON] removed some characteristics due to their view that they did not improve the prediction's performance. Therefore, our results show that the model indeed improved by including data from sex and age. In addition, we used a feature layer that allows the algorithm to distinguish those characteristics, which contributes to improving pathology's prediction. In addition, the estimated model shows that, by eliminating sex and age from the studies, the prediction decreased dramatically. [PERSON] used these variable to normalize the data in Saccade, Antisaccade, and MSaccade, to increase model's accuracy from an average of 60% to 70%. The latter is a key feature, since our estimated model uses raw data, keeping the estimation process simple. [PERSON]'s model utilized methods, especially SVMR, which require fewer parameters to optimize its performance, reducing the possibility of over-tuning training data, and  
Figure 8: Classification confusion matrix using DTI. increasing actual performance. According to the training data results, unlike ANN, the study of different configurations can change its operation and performance in general. The increase in data allows ANN to improve its performance, without changing parameters. Nevertheless, a new training improves the ANN model accuracy, unlike in the case of SVMR. However, SVMR is faster and more stable than other ANNs.