FASDetect – A machine learning-based app to screen for the risk of fetal alcohol-spectrum disorder in youth with attention-deficit/hyperactivity disorder symptoms

Abstract

AbstractFetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we developed a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit were assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We trained 6 machine learning models based on 13 selected variables and evaluated their performance. Random forests yielded the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicated that a random forest model with 6 variables – body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance – yielded equivalent predictive accuracy. We implemented the prediction model in a web-based app called FASDetect – a user-friendly, clinically scalable FASD risk calculator that is freely available at https://fasdetect.dhc-lab.hpi.de.