<![CDATA[Depression among university students is a critical mental health concern, often exacerbated by academic pressure and social adaptation. While prior studies have utilized Multi-Layer Perceptron (MLP) models to achieve up to 78% accuracy, the effectiveness of these systems remains highly sensitive to architectural design and optimization strategies. To address this gap, this study systematically evaluates the performance of modern MLP architectural variants including DenseNet, ResMLP, and ResNet paired with SGD, Adam, and RMSprop optimizers. Using a dataset of 1,025 student records, the methodology integrates Chi-Square feature selection and Min-Max normalization, followed by an 80:20 Hold-Out validation. Results demonstrate that the ResNet-RMSprop synergy yields a superior accuracy of 83.86%, significantly outperforming traditional MLP benchmarks . By identifying the optimal combination of deep learning structures and optimization algorithms, this research provides a more robust and precise technical foundation for AI-driven early detection systems in academic settings.]]>
