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- Title
Predicting Internet addiction in college students using a 1D-CNN model: analysis of influencing factors•.
- Authors
Xi Wang; Enyou Zhang; Yingjun Cui; Jie Huang; Meng Cheng
- Abstract
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.
- Subjects
CONVOLUTIONAL neural networks; INTERNET addiction; SOCIAL support; COLLEGE students; DATABASES
- Publication
Dyna, 2024, Vol 91, Issue 233, p66
- ISSN
0012-7353
- Publication type
Article
- DOI
10.15446/dyna.v91n233.112788