Abstract
Gaming addiction is a growing concern due to the increasing popularity of online and mobile games. This study aims to predict gaming addiction severity using the Random Forest classification algorithm based on behavioral and mental health factors. The dataset comprises 250 player records with 49 attributes, including gaming duration, screen time, dopamine dependency index, self-control, impulsiveness, stress, and productivity-related factors. After preprocessing, the data were divided into training and testing sets using an 80:20 stratified split. A Random Forest model with 300 trees and balanced class weighting was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved 84% accuracy, with weighted precision, recall, and F1-score of 83%, 84%, and 83%, respectively. However, macro-average precision, recall, and F1-score were 62%, 51%, and 55%, respectively, indicating limited performance on minority classes, particularly the Severe category. Feature importance identified daily playtime hours, dopamine dependency index, total screen time, and maximum consecutive gaming duration as the most important features. The findings indicate good performance on dominant classes but limited minority-class classification, highlighting the need for further data balancing.
How to Cite
| Issue | Vol. 4 No. 2 (2026) |
| Section | Articles |
| DOI | 10.58477/cj.v4i2.435 |
| Publication Date | 2026-08-30 |
| Pages | 155-164 |
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
- Costales, J. A., Catulay, J. J. J. E., Costales, J. A., & Bermudez, N. P. (2022). Kaiser-Meyer-Olkin factor analysis: A quantitative approach on mobile gaming addiction using random forest classifier. In Proceedings of the 6th International Conference on Information System and Data Mining (pp. 18–24). ACM. https://doi.org/10.1145/3546157.3546161
- Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., & He, J. (2022). Human activity recognition using wearable sensors by heterogeneous convolutional neural networks. Expert Systems with Applications, 198, 116764. https://doi.org/10.1016/j.eswa.2022.116764
- Han, J., Kamber, M., & Pei, J. (2006). Data mining: Concepts and techniques. Morgan Kaufmann.
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
- Jeong, B. J., Lee, J., Kim, H., Gwak, S., Kim, Y. K., Yoo, S. Y., Lee, D. H., & Choi, J. S. (2022). Multiple-kernel support vector machine for predicting internet gaming disorder using multimodal fusion of PET, EEG, and clinical features. Frontiers in Neuroscience, 16, 856510. https://doi.org/10.3389/fnins.2022.856510
- Kim, H. S., Son, G., Roh, E. B., Ahn, W. Y., Kim, J., Shin, S. H., Chey, J., & Choi, K. H. (2022). Prevalence of gaming disorder: A meta-analysis. Addictive Behaviors, 126, 107183. https://doi.org/10.1016/j.addbeh.2021.107183
- King, D. L., & Potenza, M. N. (2020). Gaming disorder among female adolescents: A hidden problem? Journal of Adolescent Health, 66(6), 650–652. https://doi.org/10.1016/j.jadohealth.2020.03.011
- Király, O., Potenza, M. N., & Demetrovics, Z. (2022). Gaming disorder: Current research directions. Current Opinion in Behavioral Sciences, 47, 101204. https://doi.org/10.1016/j.cobeha.2022.101204
- Kornev, D., Sadeghian, R., Nwoji, S., He, Q., Gandjbakhche, A., & Aram, S. (2022). Machine learning-based gaming behavior prediction platform. In Neuroergonomics and cognitive engineering (Vol. 42, pp. 122–128). AHFE International. https://doi.org/10.54941/ahfe1001826
- Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health, 14(3), 311. https://doi.org/10.3390/ijerph14030311
- Limone, P., Ragni, B., & Toto, G. A. (2023). The epidemiology and effects of video game addiction: A systematic review and meta-analysis. Acta Psychologica, 241, 104047. https://doi.org/10.1016/j.actpsy.2023.104047
- Niu, X., Gao, X., Zhang, M., Yang, Z., Yu, M., Wang, W., Wei, Y., Cheng, J., Han, S., & Zhang, Y. (2022). Meta-analysis of structural and functional brain alterations in internet gaming disorder. Frontiers in Psychiatry, 13, 1029344. https://doi.org/10.3389/fpsyt.2022.1029344
- Stevens, M. W. R., Dorstyn, D., Delfabbro, P. H., & King, D. L. (2021). Global prevalence of gaming disorder: A systematic review and meta-analysis. Australian & New Zealand Journal of Psychiatry, 55(6), 553–568. https://doi.org/10.1177/0004867420962851
- World Health Organization. (2019). International classification of diseases 11th revision (ICD-11). https://icd.who.int/en
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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.