Resumen

CANDO-MACHUCA, Madelayne C.; VILELA-AMAYA, Angels K.; CARTUCHE-CALVA, Joffre J.  y  HERNANDEZ-ROJAS, Dixys L.. Development of a Web and Mobile Application Using Supervised Learning Algorithms to Detect Stress and Anxiety. Espacios [online]. 2025, vol.46, n.6, pp.236-248.  Epub 30-Ene-2026. ISSN 0798-1015.  https://doi.org/10.48082/espacios-a25v46n06p20.

This study addresses the detection of stress and anxiety through physiological signals, considering their growing impact on mental health. To this end, supervised machine learning algorithms trained with the WESAD and CASE datasets were implemented, applying the CRISP-DM methodology for processing and the RAD approach in the development of a web/mobile application. As a result, the models achieved high performance in identifying emotional states, demonstrating their potential to support preventive health management.

Palabras clave : stress; anxiety; machine learning.

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