From dry eye to depression: a machine learning-based framework for predicting adolescent mental health.

BACKGROUND

Adolescent depression is a major public health concern. Physical health indicators are rarely included in risk tools. We examined whether adding dry eye disease (DED) to psychosocial and behavioral factors improves prediction of depressive symptoms.

METHODS

We analyzed 2,076 adolescent questionnaires (94.5% response) covering ocular health, sleep, electronic device use, social support, and demographics. Five machine-learning classifiers were trained with cross-validation and evaluated for discrimination and calibration.

RESULTS

Models that included DED achieved strong discrimination (AUC ≈ 0.84) and good calibration, with highest accuracy for no and severe depression and lower performance for mild/moderate categories.

CONCLUSIONS

Integrating ocular health with psychosocial factors enhances machine-learning prediction of adolescent depression and may support earlier, school-based identification and referral. Given the low-cost, questionnaire-based inputs and favorable calibration, this approach shows promise for population screening and targeted prevention, pending external validation and prospective testing.