Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network

TL;DR

This study evaluates the use of artificial neural networks to classify children with fetal alcohol spectrum disorder using various test data, achieving over 70-88% accuracy, indicating ANN's potential as a diagnostic tool.

Contribution

The paper demonstrates the effectiveness of different ANN configurations in diagnosing FASD from diverse test data, highlighting its potential in medical diagnostics.

Findings

ANN predicts 75% of outcomes with psychometric data

Models achieve over 70% accuracy with various tests

Psychometric and memory tests predict over 88% accuracy

Abstract

Fetal alcohol spectrum disorder (FASD) is a syndrome whose only difference compared to other children's conditions is the mother's alcohol consumption during pregnancy. An earlier diagnosis of FASD improving the quality of life of children and adolescents. For this reason, this study focus on evaluating the use of the artificial neural network (ANN) to classify children with FASD and explore how accurate it is. ANN has been used to diagnose cancer, diabetes, and other 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 tensor imaging (DTI)). We study the different configurations of ANN with dense layers. The first one predicts 75\% of the outcome correctly for psychometric data. The others models include a feature layer, and we used it to predict FASD using every test individually. The models accurately predict over 70\% of the cases, and psychometric and memory guides predict over 88\% accuracy. The results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. However, we could be careful in used as a diagnostic technique.