The process of qualifying work-related mental health disorders in Chile is regulated by Law No. 16.744. However, it is frequently challenged in terms of precision and objectivity. Aim: To assess the feasibility of machine learning algorithms in supporting mental health qualification. Methods: Application of a mental health disease propensity questionnaire to a sample of 340 Chilean workers. Evaluation of decision tree models and logistic regression, including enhanced versions with additional variables ("Plus") related to the intensity and temporality of stressors. Results: The "Plus Tree" model showed higher accuracy (91.2%, 95% CI: 83.9%-95.9%), particularly with balanced data (76.9%). However, there is room for improving the model's specificity due to data imbalance caused by the presence or absence of mental health condition. Conclusions: The inclusion of algorithms in the qualification process enhances efficiency and objectivity. However, further database expansion and model refinement are necessary to achieve better precision.