Identification of Latent Profiles and Determining Factors of Academic Stress in University Students: An Integrated Unsupervised–Supervised Machine Learning Approach

Academic stress is one of the main challenges affecting the psychological well-being of university students due to its impact on mental health, academic performance, and quality of life. The aim of this study was to analyze and model the factors associated with academic stress by integrating unsupervised and supervised machine learning techniques. The study was conducted with a sample of 605 students from the Universidad Nacional de San Martín (Peru), who completed validated psychometric instruments, including the PSS-10, LASSI, MBI-SS, PSQI, and A-CEA. In the first stage, dimensionality reduction and clustering techniques were applied to identify latent profiles, resulting in four distinct groups reflecting different levels of adaptation and psychological vulnerability. In the second stage, eight supervised regression models were evaluated: Linear Regression, Ridge, Lasso, Elastic Net, Random Forest, Gradient Boosting, XGBoost, and CatBoost. Lasso and Elastic Net showed virtually equivalent performance, achieving coefficients of determination (R2) close to 0.61 on the independent test set. Variable importance analysis revealed that academic burnout, sleep quality, and coping strategies were the main factors associated with perceived stress, together with contextual variables with lower relative importance. Overall, the results confirm the multidimensional nature of academic stress and show that integrating unsupervised and supervised approaches provides a more comprehensive understanding of the phenomenon in university settings.