# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
###### Abstract  
Fetal alcohol spectrum disorder (FASD) is an umbrella term for children's conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject's quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children's images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psychometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade), and 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improvement on [PERSON]'s 2019 model, which used the same data with less accuracy level. fetal alcohol spectrum; artificial neural networks; machine learning +
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to diagnose a child with FASD. The only distinct difference compared to other childhood or adolescent conditions is the mother's alcohol consumption during pregnancy [5; 6; 7]. If there is no certainty about the mother's alcohol consumption, some indirect effects are associated with FAS, such as facial dysmorphia, growth deficiency, or central nervous system abnormalities, can be used as proxy for the diagnosis. However, when none of these effects are present, the diagnosis becomes challenging [8]. In the state of the art, we can find an increased effort to develop tools on specific behavior and profiles to allow an early diagnosis of FASD, where these tools are failing to make a specific diagnosis of FASD compared to other neurodevelopmental conditions [6; 9]. Among these tools, we find tools in Machine Learning (ML) [10; 11] and Computer-Aided Detection (CAD) [12; 13]. However, there are tools to improve FASD detection [14]; therefore, this paper addresses the use of ML techniques for an improved FASD diagnosis. Specifically, this work focuses on developing computational algorithms based on ANN to classify children with FASD. The research question is whether an ANN model could be used for medical diagnosis. One important reason for testing ANN as a diagnostic tool is the easy implementation and simplicity in applying existing data. This model will be evaluated based on its performance, in terms of precision, completeness, and performance benchmark to other machine learning techniques. This model will be evaluate the goodness of fitness of using psychometric, saccadic eye movement, and diffusion tensor imaging (DTI) tests). It is believed that techniques based on machines learning are a good tool for grouping and comparing the clinical information obtained from a set of psychometric data, thus expanding the possibility of detecting differences in FASD patients. To our knowledge, the use of machine learning as FASD diagnostic tools has not been tested. However, there are current studies that have been used them, such as [PERSON] et al. (2019) [15]. Nevertheless, these authors have used support vector machine regression (SVMR) algorithms to analyze psychometric data and DTI and a linear regression for the rest of the data  
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