"Exploring the Relationship Between Depression and the Five-Factor Personality Model: A Comparative Study Using Three Decision Tree Algorithms"

Background Depression is one of the most prevalent and recurrent mental health disorders in recent years, particularly among university students, where it can significantly impact academic performance. This study aimed to explore the relationship between personality traits and depression using data mining techniques. Materials and Methods A cross-sectional study was conducted among 1,243 students at Isfahan University of Medical Sciences. Participants completed the PHQ-9 (for depression assessment), the NEO-60 (for personality traits), and a demographic questionnaire. Three widely used decision tree algorithms—CART, CHAID, and C5.0—were applied to predict depression levels. Results All three algorithms identified neuroticism as the most influential personality trait associated with depression (average rank = 87.4), followed by agreeableness, extraversion, and conscientiousness. Among the models, C5.0 demonstrated superior predictive performance (Sensitivity = 100%, Specificity = 96.8%, Accuracy = 97.5%) compared to CHAID and CART. Conclusion Decision tree algorithms offer effective tools for identifying depression based on personality traits. Neuroticism emerged as the strongest predictor, suggesting that targeted mental health interventions for students with high neuroticism scores may help reduce depression prevalence.