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Prediction of Problematic Smartphone Use: A Machine Learning Approach

Author

Listed:
  • Juyeong Lee

    (Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

  • Woosung Kim

    (College of Business Administration, Konkuk University, Seoul 05029, Korea)

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.

Suggested Citation

  • Juyeong Lee & Woosung Kim, 2021. "Prediction of Problematic Smartphone Use: A Machine Learning Approach," IJERPH, MDPI, vol. 18(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6458-:d:575056
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    References listed on IDEAS

    as
    1. Sheila Yu & Steve Sussman, 2020. "Does Smartphone Addiction Fall on a Continuum of Addictive Behaviors?," IJERPH, MDPI, vol. 17(2), pages 1-21, January.
    2. Daria J. Kuss & Mark D. Griffiths, 2011. "Online Social Networking and Addiction—A Review of the Psychological Literature," IJERPH, MDPI, vol. 8(9), pages 1-25, August.
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    Cited by:

    1. Mei-Feng Huang & Yu-Ping Chang & Wei-Hsin Lu & Cheng-Fang Yen, 2022. "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," IJERPH, MDPI, vol. 19(9), pages 1-12, May.

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