  This study presents a machine learning model based on the Naive Bayes
classifier for predicting the level of depression in university students, the
objective was to improve prediction accuracy using a machine learning model
involving 70% training data and 30% validation data based on the Naive Bayes
classifier, the collected data includes factors associated with depression from
519 university students, the results showed an accuracy of 78.03%, high
sensitivity in detecting positive cases of depression, especially at moderate
and severe levels, and significant specificity in correctly classifying
negative cases, these findings highlight the effectiveness of the model in
early detection and treatment of depression, benefiting vulnerable sectors and
contributing to the improvement of mental health in the student population.
