<![CDATA[Student mental health is a crucial factor affecting academic performance, productivity, and overall quality of life in university environments. The high prevalence of psychological disorders today demands an accurate early detection system to provide timely and efficient intervention. This study aims to develop a student mental health classification model by integrating feature engineering techniques and the Synthetic Minority Oversampling Technique (SMOTE) with the Random Forest algorithm. The feature engineering stage is conducted through the creation of a composite feature, Mental_Score, to represent students' psychological conditions more holistically and deeply. In addition, SMOTE is applied to address the data imbalance issue, making the model more sensitive in detecting the at-risk student group as the minority class. Experimental results show that the proposed model achieves an accuracy of 97%. The application of SMOTE proved effective in increasing the minority class recall to 60% and raising the F1-score from 0.57 to 0.75, significantly strengthening the detection capability for the at-risk group. Although the McNemar test yields a p-value of 1.000 due to a ceiling effect since both models are already optimal, the proposed model still offers a practical advantage in maintaining detection sensitivity. Feature importance analysis confirms that Mental_Score is the most influential attribute with a contribution value of 0.3280. This study contributes to providing a more accurate machine learning-based framework for the early detection of student mental health.]]>
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