Predictive Models in Mental Health Based on Unsupervised Data Clustering

In the university context, students’ mental health has been progressively affected over time. The objective of this research was to develop a predictive model of machine learning based on the K-Means algorithm, with the purpose of identifying and classifying mental health profiles among university students. For the construction of this model, the standard Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied, which encompasses five stages: business understanding, data understanding, data preparation, modeling, and evaluation. The results obtained suggest that the generated clusters produce consistent groupings in key variables such as screen time, hours of sleep, and level of physical activity, allowing the characterization of different student profiles. This approach provides valuable information for designing academic support strategies and programs aimed at students’ well-being and mental health. The early identification of behavioral patterns and lifestyle habits enables educational institutions to implement preventive and personalized measures, fostering improved academic performance and university adaptation.