Exploratory machine learning models for computer vision syndrome in occupational health.

Background: Computer vision syndrome (CVS) is a growing occupational health concern linked to extensive digital device use, yet robust predictive models for risk identification remain scarce. This study aimed to evaluate and compare traditional and machine learning-based predictive models for CVS by assessing their accuracy in identifying CVS cases as defined by the CVS-Q Methods: A cross-sectional study was conducted among 90 Portuguese workers regularly exposed to digital display devices. Data were collected via a self-administered questionnaire covering age, sex, refractive errors, daily screen time, and symptoms assessed with the validated CVS-Q Results: Logistic regression and XGBoost demonstrated the highest accuracy (70.6%) and strong discriminative ability (AUC = 0.773 and 0.758, respectively). All models exhibited high sensitivity (≥72.7%), though specificity varied from 16.7% (random forest) to 50% (SVM, XGBoost). Calibration was acceptable across models (Brier scores 0.21-0.25). Female sex was the only statistically significant predictor in logistic regression (OR = 5.14, Conclusions: Our results indicate that predictive modeling shows promise for CVS risk identification in occupational settings, particularly using demographic and behavioral variables. However, the limited and unrepresentative sample warrants caution, and further research with larger cohorts is needed to confirm these preliminary findings and assess their scalability.