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Fetal alcohol spectrum disorder (FASD) is a syndrome whose only differencecompared to other children's conditions is the mother's alcohol consumptionduring pregnancy. An earlier diagnosis of FASD improving the quality of life ofchildren and adolescents. For this reason, this study focus on evaluating theuse of the artificial neural network (ANN) to classify children with FASD andexplore how accurate it is. ANN has been used to diagnose cancer, diabetes, andother diseases in the medical area, being a tool that presents good results.The data used is from a battery of tests from children for 5-18 years old(include tests of psychometric, saccade eye movement, and diffusion tensorimaging (DTI)). We study the different configurations of ANN with dense layers.The first one predicts 75\% of the outcome correctly for psychometric data. Theothers models include a feature layer, and we used it to predict FASD usingevery test individually. The models accurately predict over 70\% of the cases,and psychometric and memory guides predict over 88\% accuracy. The resultssuggest that the ANN approach is a competitive and efficient methodology todetect FASD. However, we could be careful in used as a diagnostic technique.

Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network