Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth development data (2019-2022).

ABSTRACT:

Background

Internet Addiction (IA) has emerged as a critical concern, especially among school age children and adolescents, potentially stalling their physical and mental development. Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young's Internet Addiction Test.

Methods

The longitudinal data consisting of 8,824 schoolchildren from the Chengdu Positive Child Development (CPCD) survey were analyzed, where 33.3% of participants were identified with IA (Age: 10.97 ± 2.31, Male: 51.73%). IA was defined using Young's Internet Addiction Test (IAT ≥ 40). Demographic variables such as age, gender, and grade level, a

SUBMITTER: Liu J 

PROVIDER: S-EPMC12307306 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature