Use of Neuronal Network for Early Detection of Depression for University Students

This study explores the use of multilayer neural networks for the early detection of depression in university students. These neural networks analyze large volumes of data, including emotional patterns detected through questionnaires, enabling precise identification of depressive signs. The research highlights the effectiveness of these advanced architectures in generating early diagnoses and their relevance in mitigating associated problems, such as poor academic performance and the risk of dropout. Despite challenges such as variability in behavioral patterns and data quality, the results suggest that multilayer neural networks are valuable tools for monitoring mental health in this population. The need for continuous improvement of algorithms and the expansion of dataset diversity is emphasized to ensure these models remain relevant and effective in the mental health context of university students.