Prediction of Problematic Smartphone Use: A Machine Learning Approach
Lee J, Kim W
International journal of environmental research and public health · 11 citations
How it was studied
- Design
- Cross-sectional study (classified by our AI screen)
- Studied in
- People
- Main outcome
- Health markers and function
Who paid for it
- Funding
- Independent funding
Based on full-text disclosure statement.
Publication
- Published
- 2021-06-15 · Int J Environ Res Public Health · vol. 18 · issue 12 · p. 6458
- Publisher
- Multidisciplinary Digital Publishing Institute
- Cited
- 38 citations · more than 94% of similar papers · 4.3× the field average
- Impact
- Top 10% most cited in its field
- References
- 42 works
- Access
- Open access (journal) · CC-BY
- Research areas
- Impact of Technology on Adolescents · Digital Mental Health Interventions · Mental Health via Writing
- Keywords
- smartphone addiction, addiction prediction, Smartphone Addiction Scale, random forest, XGBoost, decision tree, user characteristics
- MeSH
- republic of korea, machine learning, smartphone, cell phone, internet addiction disorder
2 authors
From KR
- Juyeong LeeUlsan National Institute of Science and Technology
- Woosung Kim · correspondingKonkuk University
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.
Abstract via Europe PMC. Copyright remains with the authors or publisher (CC BY).
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