Early Mental Health Detection with Machine Learning: A Practical Approach to Model Development and Implementation

Abstrak

Mental health, encompassing emotional, psychological, and social well-being, plays a pivotal role in individuals’ academic and personal success. Among undergraduate students, the prevalence of mental health disorders such as anxiety, depression, and stress has escalated in response to academic pressures, lifestyle transitions, and socioeconomic factors. Early detection is vital for mitigating severe repercussions, including diminished academic performance and increased risk of self-harm. Leveraging the Depression, Anxiety, and Stress Scale-42 (DASS-42), this study employs machine learning (ML) methods—specifically Support Vector Machine (SVM) and Random Forest (RF)—to analyze mental health patterns under a two-class scenario (“Good” vs. “Counseling”). A balanced dataset was obtained through the Synthetic Minority Over-Sampling Technique (SMOTE), ensuring fairer representation of minority “Counseling” cases. The results demonstrate that both SVM and RF excel in detecting depression, each achieving near-perfect accuracy, precision, and recall (0.97). This performance suggests that the discriminative power of the features for depression is sufficiently robust to enable clear classification boundaries with minimal misclassification. For anxiety, SVM attains a slightly higher accuracy (0.91) compared to RF (0.90), largely driven by SVM’s superior precision (0.94) when identifying “Good” cases. In stress prediction, SVM again outperforms RF (0.95 vs. 0.93 accuracy), although RF showcases near-perfect precision and recall for “Counseling.” Despite these successes, both models occasionally falter when distinguishing borderline cases, indicating that feature overlap and remaining class imbalance still pose challenges. In conclusion, SVM and RF provide promising avenues for early mental health detection in university settings, highlighting the potential of ML-driven tools to inform targeted interventions. Future research should focus on expanding feature sets (e.g., integrating behavioral or physiological data) and refining sampling strategies to enhance minority-class detection.