ArticleInternational journal of environmental research and public health2021
Prediction of Problematic Smartphone Use: A Machine Learning Approach.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- Prediction of smartphone overdependence and analysis of its influencing factors among older adults based on machine learning.Frontiers in psychology · 2026Article
- Does learning burnout link physical exercise and problematic short-video use? Evidence from a three-wave random-intercept cross-lagged panel model and supplementary machine-learning analysis among Chinese high school students.Frontiers in psychology · 2026Article
- Protocol for AI-based prediction of problematic digital technology use among Indian youth: a Centre for Advanced Research on Addictive Behaviours initiative.Frontiers in psychology · 2026Article
- Classifying Internet Addiction Using Machine Learning Approach: A Study Among Adolescents in Bangladesh.Public health challenges · 2025Article
- From dry eye to depression: a machine learning-based framework for predicting adolescent mental health.BMC medical informatics and decision making · 2025Article
- Adolescent Smartphone Overdependence in South Korea: A Place-Stratified Evaluation of Conceptually Informed AI/ML Modeling.International journal of environmental research and public health · 2025Article
- Risk level prediction for problematic internet use: A digital health perspective.Internet interventions · 2025Article
- The Relationship Between Smartphone and Game Addiction, Leisure Time Management, and the Enjoyment of Physical Activity: A Comparison of Regression Analysis and Machine Learning Models.Healthcare (Basel, Switzerland) · 2025Article
- Problematic Smartphone Use and Its Associations with Sexual Minority Stressors, Gender Nonconformity, and Mental Health Problems among Young Adult Lesbian, Gay, and Bisexual Individuals in Taiwan.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While smartphone addiction is becoming a recent concern with the exponential increase in the number of smartphone users, it is difficult to predict problematic smartphone users based on the usage characteristics of individual smartphone users. This study aimed to explore the possibility of predicting smartphone addiction level with mobile phone log data. By Korea Internet and Security Agency (KISA), 29,712 respondents completed the Smartphone Addiction Scale developed in 2017. Integrating basic personal characteristics and smartphone usage information, the data were analyzed using machine learning techniques (decision tree, random forest, and Xgboost) in addition to hypothesis tests. In total, 27 variables were employed to predict smartphone addiction and the accuracy rate was the highest for the random forest (82.59%) model and the lowest for the decision tree model (74.56%). The results showed that users' general information, such as age group, job classification, and sex did not contribute much to predicting their smartphone addiction level. The study can provide directions for future work on the detection of smartphone addiction with log-data, which suggests that more detailed smartphone's log-data will enable more accurate results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.