Abstract
This study develops an intelligent system based on machine learning for the early detection of academic stress in university students, focusing on the areas of Engineering and Social Sciences. Based on a quantitative and simulation-based approach, a structured dataset was built integrating academic, behavioral, and psychosocial variables. Predictive models were implemented, including Random Forest, Support Vector Machines, and XGBoost, and their performance was evaluated using multiclass and binary classification metrics. The results show that ensemble models achieved the highest levels of accuracy, exceeding 90% in the detection of stress levels. Likewise, key variables such as fatigue, concentration, pending assignments, and hours of sleep were identified. Scenario simulation demonstrated that combined interventions generate significant reductions in the probability of high stress. The study provides a predictive-explanatory approach that contributes to decision-making in educational environments oriented toward student well-being.
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