Predicting Depression Risk Using Machine Learning Among Students

Depression in students is a serious public health issue that affects their learning and daily life activities. Early detection of students who are at risk is important however many studies focus on high accuracy without checking fairness and interpretability. This study developed a transparent and fairness aware framework to predict the risk of non-clinical depression using publicly available Kaggle Dataset from 27,900 anonymized student survey answers. Three models were evaluated which are Logistic Regression, Random Forest, and XGBoost. The models showed very similar performance with accuracy around 84.5% to 84.8%. XGBoost had a slight difference with a ROC AUC of 0.923 but Logistic Regression was almost similar at 0.922, suggesting there’s little difference between the two in terms of overall performance suggesting that the data patterns are simple not complex.A post hoc gender fairness evaluation showed minimal performance differences across male and female students. SHAP and LIME explanations revealed that psychosocial stress factors, particularly suicidal thoughts, academic pressure and financial stress, were the strongest contributors to predicted risk. In summary, this research shows that fairness evaluation and interpretability as well as prediction accuracy could be Incorporated into one responsible framework for student mental health risk screening embedding ethical consideration.