Application of machine learning in predicting adolescent Internet behavioral addiction
The detection rate of Internet addiction is higher in males than in females, and adolescents with impulsive, extroverted, psychotic, neurotic, obsessive, depressive, and hostile traits are more prone to developing Internet addiction. While the overall performance of the machine learning models for predicting adolescent Internet addiction is moderate, the extreme gradient boosting method outperforms others, effectively identifying risk factors and enabling targeted interventions.
Authors
- Wo Wang (ORCID: https://orcid.org/0009-0002-9442-2097)
- Xiao‐Ming Xu (ORCID: https://orcid.org/0000-0002-7229-0081)
- Yao Gan
- Hong Su (ORCID: https://orcid.org/0000-0002-4673-6449)
- Ming Ai
- Jian Mei Chen
- Qi Zhang (ORCID: https://orcid.org/0000-0003-3617-5892)
- Li Kuang (ORCID: https://orcid.org/0000-0002-3265-2252)
- Jun Cao
- Jing-Lan He (ORCID: https://orcid.org/0000-0003-0217-1287)
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-04-01
- DOI
- https://doi.org/10.3389/fpsyt.2024.1521051
- Citations
- 7
- Primary Topic
- Impact of Technology on Adolescents
- Type
- article
- Field-Weighted Citation Impact
- 29.41
Funders
- National Natural Science Foundation of China
- Chongqing Science and Technology Commission