Applied Unsupervised Learning for Pattern Recognition of Depression cases within a Young Adult Population

Authors

  • Octavio Mendoza 1Benemerita Universidad Autónoma de Puebla
  • Mireya Tovar Vidal Benemérita Universidad Autónoma de Puebla
  • Meliza Contreras Benemérita Universidad Autónoma de Puebla

DOI:

https://doi.org/10.13053/cys-29-1-5502

Keywords:

Clustering, pattern recognition, depression

Abstract

This study uses unsupervised learning to identify depression patterns in Mexican university students. By analyzing demographic, academic, and psychological factors, it aims to uncoversub groups with similar depression profiles and identify risk and protective factors. The study compares clustering algorithms and evaluates their performance using metrics like the Silhouette Coefficient and Davies-Bouldin Index. This research contributes to the field of machine learning in mental health and may improve support services for students at risk of depression.

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Published

2025-03-25

Issue

Vol. 29 No. 1 (2025): 29(1) 2025

Section

Articles of the Thematic Section

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