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Predicción y análisis explicable del estrés estudiantil basado en árboles de decisión mejorados

Cheng Liu1, Shuang Yu1

  • 1Department of Digital Business, Jiangsu Vocational Institute of Commerce, Nanjing, Jiangsu, China.

Frontiers in psychology
|January 19, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio desarrolló un modelo optimizado de árbol de decisión para predecir el estrés estudiantil, logrando una precisión del 92,7%. Los factores clave que influyen en el estrés identificados fueron la presión arterial, el apoyo social y la depresión.

Palabras clave:
modelo SHAPalgoritmo de árbol de decisiónoptimización de halcones de Harrisaprendizaje automáticopredicción de estrés estudiantil

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Área de la Ciencia:

  • Psicología Educativa
  • Ciencias Sociales Computacionales
  • Informática de la Salud Mental

Sus antecedentes:

  • Las poblaciones estudiantiles enfrentan presiones significativas relacionadas con lo académico, lo social y la carrera profesional.
  • La predicción precisa del nivel de estrés y la identificación de los factores que influyen son cruciales para el bienestar estudiantil.

Objetivo del estudio:

  • Desarrollar y validar un modelo mejorado de aprendizaje automático para predecir el estrés estudiantil.
  • Identificar los factores clave que contribuyen al estrés estudiantil.

Principales métodos:

  • Se compararon nueve algoritmos de aprendizaje automático para seleccionar un modelo base óptimo.
  • Se mejoró un modelo de árbol de decisión (DT) utilizando el algoritmo de optimización de halcones de Harris (HHO).
  • Se aplicaron Explicaciones Aditivas de Shapley (SHAP) para la interpretación del modelo y el análisis de características.

Principales resultados:

  • El algoritmo de árbol de decisión (DT) logró una precisión de predicción del 0,909.
  • El modelo optimizado de árbol de decisión con optimización de halcones de Harris (HHO-DT) mejoró la precisión al 0,927 con menos clasificaciones erróneas.
  • Las Explicaciones Aditivas de Shapley (SHAP) identificaron la presión arterial, el apoyo social y la depresión como los principales predictores del estrés estudiantil.

Conclusiones:

  • El modelo HHO-DT ofrece una herramienta científicamente eficaz para predecir el estrés estudiantil.
  • Los hallazgos respaldan intervenciones específicas por parte de educadores, padres y estudiantes para promover la salud mental.
  • La predicción precisa del estrés puede ayudar en el desarrollo de estrategias para aliviar la presión estudiantil y mejorar el bienestar general.