A Machine Learning Model for Rapid Prediction of Smart Media Addiction Tendencies Based on Survey Data 


Vol. 14,  No. 5, pp. 297-304, May  2025
https://doi.org/10.3745/TKIPS.2025.14.5.297


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  Abstract

This study proposes an artificial intelligence model that utilizes machine learning to rapidly classify adults into three categories— high-risk, potential risk, and normal—based on big data from a survey of smartphone addiction. Smart media continues to evolve, and its reliance has surged significantly since the COVID-19 pandemic; however, current prevention and treatment programs for smartphone addiction are insufficient. Most smart media users are unaware of their addiction, and even when they recognize the potential risks of addiction, the prediction of addictive tendencies primarily occurs by presenting scales to clients or visitors seeking psychological assessments. As a result, the general public's access to assessments for smartphone addiction scales is notably limited. This study aims to enhance public access to smartphone addiction scales by developing an automated artificial intelligence model using machine learning, in order to identify addiction trends on both individual and group levels. The artificial intelligence model proposed in this study enhances accuracy and processing time by substituting the score processing of traditional scales with machine learning techniques. The results of this study can contribute to real-time recognition and prevention of smartphone addiction by reducing manpower and time costs during the classification process, thereby promoting healthier usage habits through the reduction of addiction risks. In counseling clients, an AI-based approach can be utilized for the development of personalized prevention and treatment programs for addiction. Moreover, the collected data will serve as an important foundational resource for understanding trends in smartphone addiction as big data.

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[IEEE Style]

H. S. Hyeon, K. G. Hyuk, L. S. Jin, K. Jongwan, "A Machine Learning Model for Rapid Prediction of Smart Media Addiction Tendencies Based on Survey Data," The Transactions of the Korea Information Processing Society, vol. 14, no. 5, pp. 297-304, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.5.297.

[ACM Style]

Hong Seung Hyeon, Kwon Gi Hyuk, Lee Seo Jin, and Kim Jongwan. 2025. A Machine Learning Model for Rapid Prediction of Smart Media Addiction Tendencies Based on Survey Data. The Transactions of the Korea Information Processing Society, 14, 5, (2025), 297-304. DOI: https://doi.org/10.3745/TKIPS.2025.14.5.297.

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