Explainable prediction of problematic smartphone use among South Korea's children and adolescents using a Machine learning approach

被引:0
作者
Kim, Kyungwon [1 ]
Yoon, Yoewon [2 ]
Shin, Soomin [3 ]
机构
[1] Incheon Natl Univ, Sch Int Trade & Business, Incheon 22012, South Korea
[2] Dongguk Univ, Dept Social Welf, Seoul 04620, South Korea
[3] Yuhan Univ, Dept Social Serv, Bucheon 14780, South Korea
关键词
Problematic Smartphone Use; Explainable Prediction; Machine Learning; Smartphone Dependency; ARTIFICIAL-INTELLIGENCE; INTERNET ADDICTION; RISK-FACTORS; QUALITY; ANXIETY;
D O I
10.1016/j.ijmedinf.2024.105441
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Background: Korea is known for its technological prowess, has the highest smartphone ownership rate in the world at 95%, and the smallest gap in smartphone ownership between generations. Since the onset of the COVID19 pandemic, problematic smartphone use is becoming more prevalent among Korean children and adolescent owing to limited school attendance and outdoor activities, resulting in increased reliance on smartphones. 40.1% of adolescents are classified as high-risk, with only the adolescent group showing a persistent rise year after year. Objective: The study purpose is to present data-driven analysis results for predicting and preventing smartphone addiction in Korea, where problematic smartphone use is severe. Participants and Methods: To predict the risk of problematic smartphone use in Korean children and adolescents at an early stage, we used data collected from the Smartphone Overdependence Survey conducted by the National Information Society Agency between 2017 and 2021. Eight representative machine and deep learning algorithms were used to predict groups at high risk for smartphone addiction: Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), extreme Gradient Boosting (XGBoost), Light GBM, Categorical Boosting, Multilayer Perceptron, and Convolutional Neural Network. Results: The XGBoost ensemble algorithm predicted 87.60% of participants at risk of future problematic smartphone usebased on precision. Our results showed that prolonged use of games, webtoons/web novels, and ebooks, which have not been found in previous studies, further increased the risk of problematic smartphone use. Conclusions: Artificial intelligence algorithms have potential predictive and explanatory capabilities for identifying early signs of problematic smartphone use in adolescents and young children. We recommend that a variety of healthy, beneficial, and face-to-face activities be offered as alternatives to smartphones for leisure and play culture.
引用
收藏
页数:14
相关论文
共 53 条
[31]   Difficulties faced by university students with self-reported symptoms of attention-deficit hyperactivity disorder: a qualitative study [J].
Kwon, Soo Jin ;
Kim, Yoonjung ;
Kwak, Yeunhee .
CHILD AND ADOLESCENT PSYCHIATRY AND MENTAL HEALTH, 2018, 12
[32]   Does Parental Control Work With Smartphone Addiction?: A Cross-Sectional Study of Children in South Korea [J].
Lee, Eun Jee ;
Ogbolu, Yolanda .
JOURNAL OF ADDICTIONS NURSING, 2018, 29 (02) :128-138
[33]   Risk Factors for Smartphone Addiction in Korean Adolescents: Smartphone Use Patterns [J].
Lee, Hyuk ;
Kim, Jun Won ;
Choi, Tae Young .
JOURNAL OF KOREAN MEDICAL SCIENCE, 2017, 32 (10) :1674-1679
[34]   Psychological Factors Associated With Smartphone Addiction in South Korean Adolescents [J].
Lee, Jeewon ;
Sung, Min-Je ;
Song, Sook-Hyung ;
Lee, Young-Moon ;
Lee, Je-Jung ;
Cho, Sun-Mi ;
Park, Mi-Kyung ;
Shin, Yun-Mi .
JOURNAL OF EARLY ADOLESCENCE, 2018, 38 (03) :288-302
[35]   Prediction of Problematic Smartphone Use: A Machine Learning Approach [J].
Lee, Juyeong ;
Kim, Woosung .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (12)
[36]   Dependency on Smartphone Use and Its Association with Anxiety in Korea [J].
Lee, Kyung Eun ;
Kim, Si-Heon ;
Ha, Tae-Yang ;
Yoo, Young-Myong ;
Jung, Jai-Hyuk ;
Jang, Jae-Yeon .
PUBLIC HEALTH REPORTS, 2016, 131 (03) :411-419
[37]   Association between smartphone overdependence and generalized anxiety disorder among Korean adolescents [J].
Lee, Yeon-Suk ;
Joo, Jae Hong ;
Shin, Jaeyong ;
Nam, Chung Mo ;
Park, Eun-Cheol .
JOURNAL OF AFFECTIVE DISORDERS, 2023, 321 :108-113
[38]   Explainable AI: A Review of Machine Learning Interpretability Methods [J].
Linardatos, Pantelis ;
Papastefanopoulos, Vasilis ;
Kotsiantis, Sotiris .
ENTROPY, 2021, 23 (01) :1-45
[39]   Parental technoference and smartphone addiction in Chinese adolescents: The mediating role of social sensitivity and loneliness [J].
Liu, Qinxue ;
Wu, Jiayin ;
Zhou, Zongkui ;
Wang, Weijun .
CHILDREN AND YOUTH SERVICES REVIEW, 2020, 118
[40]  
Lundberg SM, 2017, ADV NEUR IN, V30