Perfiles de riesgo de salud mental en estudiantes universitarios mediante GHQ-28 y técnicas de Machine Learning

Doris Paola Sanabria Garcia; Jesus Antonio Villarraga

doi:10.22490/25394088.10120

Vol. 19 No. 2 (2025)

Published 2025-11-25

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Article

Profiles of Mental Health Risk in University Students Using GHQ-28 and Machine Learning Techniques

DOI: https://doi.org/10.22490/25394088.10120

Doris Paola Sanabria Garcia National University of Colombia

Jesus Antonio Villarraga National University of Colombia

Pre-print (Spanish)

Abstract
References

University students’ psychological well-being is essential for their academic persistence and performance. This study aimed to identify and characterize psychological risk profiles among students of Agricultural Engineering and Social Sciences at the University of Cundinamarca, using the General Health Questionnaire (GHQ-28), which assesses four subscales: somatization, anxiety/insomnia, social dysfunction, and severe depression. This quantitative research, with a descriptive, exploratory, and predictive approach, included 227 students who voluntarily completed the instrument. Responses were coded using the Likert method (0-1-2-3) and analyzed along with sociodemographic variables. Descriptive statistics, dimensionality reduction through Principal Component Analysis (PCA), and unsupervised clustering with K-Means identified three distinct risk profiles: low, intermediate, and high. For predictive classification, logistic regression and Random Forest models were implemented, with logistic regression showing superior performance (AUC = 0.96) compared to Random Forest (AUC = 0.57). Findings support the design of targeted intervention strategies and highlight the GHQ-28’s utility as a screening tool in university settings.

keywords: Mental health , GHQ-28, somatization, anxiety, social dysfunction, depression, PCA, K-Means, logistic regression

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How to Cite

Sanabria Garcia, D. P., & Villarraga, J. A. (2025). Profiles of Mental Health Risk in University Students Using GHQ-28 and Machine Learning Techniques. Publicaciones E Investigación, 19(2). https://doi.org/10.22490/25394088.10120

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