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Predicting Internet addiction in college students using a 1D-CNN model: analysis of influencing factors
Predicción de la adicción a Internet en estudiantes universitarios mediante un modelo 1D-CNN: análisis de los factores influyentes
DOI:
https://doi.org/10.15446/dyna.v91n233.112788Palabras clave:
internet addiction, 1D-CNN, predicting, college students, model (en)adicción a Internet, red neuronal profunda, predicción, estudiantes universitarios, modelo (es)
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This study constructs a deep learning-based model to predict internet addiction among college students and analyzes significant influencing factors. A random survey of 4,895 students from a university in Shandong Province was conducted using questionnaires on general information, internet addiction (CIAS-R), personality (CBF-PI-B), psychological traits (SDS, SAS), parenting styles (EMBU), behavioral issues (SAS-C), and social support (ASSRS) to establish a database. A predictive model was developed using a 1D Convolutional Neural Network (1D-CNN), extracting key influencing factors of internet addiction. The model showed 92.77% accuracy, with high precision and recall rates for predicting normal users and addicts. The gradient calculation indicates that in second-year students, negative and withdrawal behaviors, depression, over-interfering families, and anxiety significantly contribute to Internet addiction, with factors exceeding 0.5. The 1D-CNN model offers robust performance and accuracy in predicting internet addiction, identifying significant factors for early prevention and potential integration with apps for real-time monitoring.
Este estudio construye un modelo basado en el aprendizaje profundo para predecir la adicción a Internet entre los estudiantes universitarios y analiza los factores influyentes significativos. Se realizó una encuesta aleatoria a 4.895 estudiantes de una universidad de la provincia de Shandong mediante cuestionarios sobre información general, adicción a internet (CIAS-R), personalidad (CBF-PI-B), rasgos psicológicos (SDS, SAS), estilos parentales (EMBU), problemas de conducta (SAS-C) y apoyo social (ASSRS) para establecer una base de datos. Se desarrolló un modelo predictivo utilizando una red neuronal convolucional 1D (1D-CNN), extrayendo los factores clave que influyen en la adicción a Internet. El modelo mostró una exactitud del 92,77%, con altos índices de precisión y recuerdo para predecir usuarios normales y adictos. El cálculo del gradiente indica que, en los estudiantes de segundo curso, los comportamientos negativos y de retraimiento, la depresión, el exceso de interferencia familiar y la ansiedad contribuyen significativamente a la adicción a Internet, con factores superiores a 0,5. El modelo 1D-CNN ofrece un rendimiento y una precisión robustos en la predicción de la adicción a Internet, identificando factores significativos para la prevención temprana y la integración potencial con apps para la monitorización en tiempo real.
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