Machine Learning-Based Gambling Addiction Risk Prediction Model 


Vol. 14,  No. 6, pp. 417-423, Jun.  2025
https://doi.org/10.3745/TKIPS.2025.14.6.417


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  Abstract

This paper proposes a machine learning-based prediction model to distinguish gambling addicts according to their level of addiction to mitigate gambling-related problems. With the widespread use of the Internet and smartphones, gambling accessibility has increased, leading to a growing number of individuals experiencing gambling addiction. Gambling addiction is not just a personal issue but a significant societal problem that requires early detection and intervention. To prevent gambling problems and strengthen treatment interventions, a model capable of quickly and accurately identifying gambling addicts is necessary. In this study, we propose a method to predict addiction risk levels based on an individual’s gambling behavior using a machine learning model trained on data from the Korean version of the Canadian Problem Gambling Index (K-CPGI). We compared and analyzed various machine learning models and found that logistic regression demonstrated relatively high performance. Considering its interpretability and reliability, it was selected as the final model. However, due to the limitations of the dataset used in this study, we discuss the need for additional data collection and validation of generalization performance. Through this research, we expect to enhance the early and accurate identification of gambling addicts, thereby strengthening treatment linkage and contributing to the development of gambling addiction prevention and intervention strategies.

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[IEEE Style]

L. S. Jin, C. J. Woong, H. S. Hyeon, K. Jongwan, "Machine Learning-Based Gambling Addiction Risk Prediction Model," The Transactions of the Korea Information Processing Society, vol. 14, no. 6, pp. 417-423, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.6.417.

[ACM Style]

Lee Seo Jin, Choi Ji Woong, Hong Seung Hyeon, and Kim Jongwan. 2025. Machine Learning-Based Gambling Addiction Risk Prediction Model. The Transactions of the Korea Information Processing Society, 14, 6, (2025), 417-423. DOI: https://doi.org/10.3745/TKIPS.2025.14.6.417.

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