Abstract
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.
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Acknowledgments
The authors would like to thank Benemerita Universidad Autonoma de Puebla. The present work was funded by the research project 00082 at VIEP-BUAP 2024 and by the Consejo Nacional de Humanidades de Ciencia y Tecnologia (CONAHCYT) with scholarship number 1126315.
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Mendoza Gómez, O., Tovar Vidal, M., Contreras González, M. (2024). Detection of Depression Symptoms Through Unsupervised Learning. In: Mezura-Montes, E., Acosta-Mesa, H.G., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2024. Lecture Notes in Computer Science, vol 14755. Springer, Cham. https://doi.org/10.1007/978-3-031-62836-8_21
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