Predicting probable eating disorders in Chinese adolescents using longitudinal data: A comparison between traditional machine learning and modern deep learning approaches

Eating disorders (EDs) are serious psychiatric conditions, and earlier prediction of ED risk may reduce untreated illness and complications. We compared traditional machine learning (ML) models, including logistic regression, decision trees, support vector machines, and Extreme Gradient Boosting (XGBoost), with two deep learning (DL) models (i.e., xDeepFM, FGCNN+xDeepFM) for predicting probable EDs among Chinese adolescents. Using a four-wave (18-month) longitudinal dataset, we predicted probable ED status at follow-up (T4) from risk factors assessed at T1–T3, with models trained and evaluated separately for females and males. Performance was assessed with accuracy, AUC, and F1-score. XGBoost achieved the best overall performance across sexes (accuracy = .9099, AUC = .9472, F1 = .6314) and outperformed the evaluated DL models. The hybrid DL model (FGCNN+xDeepFM) showed the second-highest performance. Feature-importance analyses indicated that prior eating disturbance was the strongest predictor for both sexes. Among females, the next highest-ranked predictors were body shame, peer appearance pressure, and weight bias internalization; among males, they were weight bias internalization, psychological distress, and family appearance pressure. Thus, traditional machine learning, particularly XGBoost, may support early detection of probable ED risks, while DL approaches were competitive but not consistently superior. Sex-specific profiles may inform tailored prevention.