Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder

Abstract

Background: early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD).

Methods: case-control samples (aged 18–25 years), including participants with Anorexia Nervosa (AN), Bulimia Nervosa (BN), MDD, AUD, and matched controls, were used for diagnostic classification. For risk prediction, we used a longitudinal population-based sample (IMAGEN study), assessing adolescents at ages 14, 16 and 19. Regularized logistic regression models incorporated broad data domains spanning psychopathology, personality, cognition, substance use, and environment.

Results: the classification of EDs was highly accurate, even when excluding body mass index from the analysis. The area under the receiver operating characteristic curves (AUC-ROC [95 % CI]) reached 0.92 [0.86–0.97] for AN and 0.91 [0.85–0.96] for BN. The classification accuracies for MDD (0.91 [0.88–0.94]) and AUD (0.80 [0.74–0.85]) were also high. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75–0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. In the longitudinal population sample, the models exhibited moderate performance in predicting the development of future ED symptoms (0.71 [0.67–0.75]), depressive symptoms (0.64 [0.60–0.68]), and harmful drinking (0.67 [0.64–0.70]).

Conclusions: our findings demonstrate the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.

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