"Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence"

Early detection and prevention of eating disorders (EDs) in adolescence are crucial yet challenging. We developed and validated diagnostic and prognostic models to predict EDs using data from 44,357 Danish National Birth Cohort participants. Models were trained to identify ED presence in early and late adolescence (11- and 18-year follow-up), utilizing approximately 100 predictors from self-reported and registry-based data. The machine learning model demonstrated strong discrimination for both tasks (diagnostic Area Under the receiver operating characteristic Curve = 81.3; prognostic AUC = 76.9), while a logistic regression model using the top 10 predictors achieved comparable performance. Sex, emotional symptoms, peer relationship and conduct problems, stress levels, parental BMI values, body dissatisfaction, and BMI at the 7-year follow-up emerged as key predictors. Our models showed potential utility in supporting clinical risk assessment, particularly for low-risk preventive interventions, though further validation studies are needed to evaluate their effectiveness in real-world clinical settings.