Detection of Depression Symptoms Through Unsupervised Learning
摘要
An increase in the decline of mental health in student populations has been observed since 2019. The objective of this study is to characterize the depression levels in university students from the Computer Science area of BUAP. The CES-D Scale was used and unsupervised algorithms K-Means, AGNES and DKM were applied for the grouping and characterization of the depression levels. The results show the symptoms that lead to a specific depression case.