<![CDATA[Early identification of depression risk among students is increasingly important for educational institutions because mental health problems may affect academic engagement, well-being, and learning continuity. This study aims to compare TabNet and CatBoost in predicting student depression risk and to examine their explainability in identifying influential predictors from structured tabular data. The dataset used in this study consists of student-related variables covering personal characteristics, academic conditions, psychological indicators, and lifestyle factors. The experimental procedure included data cleaning, missing value treatment, categorical feature transformation, feature scaling, model training, testing, and interpretation. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Meanwhile, model explainability was examined through attention-based feature importance for TabNet and SHAP-based interpretation for CatBoost. The experimental results indicate that CatBoost produced better overall classification performance, achieving 84.54% accuracy compared with 83.44% for TabNet. CatBoost also obtained higher precision, F1-score, and ROC-AUC values. In contrast, TabNet showed slightly better recall, suggesting stronger sensitivity in detecting students classified as at risk. The interpretation results show that suicidal thoughts, financial stress, academic pressure, sleep duration, study satisfaction, dietary habits, and workload-related variables were consistently relevant to the prediction process. These findings indicate that model selection for student depression prediction should consider not only accuracy, but also sensitivity and interpretability.]]>
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