已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

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

人工智能 智能手机成瘾 机器学习 逻辑回归 随机森林 出勤 朴素贝叶斯分类器 Boosting(机器学习) 计算机科学 心理学 上瘾 应用心理学 支持向量机 经济增长 经济 神经科学
作者
Kyungwon Kim,Yoewon Yoon,Soomin Shin
出处
期刊:International Journal of Medical Informatics [Elsevier BV]
卷期号:186: 105441-105441 被引量:10
标识
DOI:10.1016/j.ijmedinf.2024.105441
摘要

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 COVID-19 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. 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. 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 e-books, which have not been found in previous studies, further increased the risk of problematic smartphone use. 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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Lucky完成签到 ,获得积分10
1秒前
1秒前
二一完成签到 ,获得积分10
1秒前
gstaihn完成签到,获得积分10
2秒前
2秒前
2秒前
JamesPei应助汪佳璇采纳,获得150
2秒前
soundscapy发布了新的文献求助50
3秒前
soundscapy发布了新的文献求助10
3秒前
soundscapy发布了新的文献求助10
3秒前
3秒前
4秒前
4秒前
小格格完成签到,获得积分10
4秒前
友好的白云完成签到,获得积分10
5秒前
5秒前
Ryan完成签到 ,获得积分10
6秒前
6秒前
soundscapy发布了新的文献求助10
7秒前
7秒前
soundscapy发布了新的文献求助10
7秒前
整齐白萱完成签到,获得积分10
7秒前
soundscapy发布了新的文献求助10
7秒前
7秒前
小巧凝竹发布了新的文献求助10
7秒前
soundscapy发布了新的文献求助10
7秒前
soundscapy发布了新的文献求助10
7秒前
soundscapy发布了新的文献求助10
8秒前
9秒前
xuan完成签到 ,获得积分10
9秒前
9秒前
10秒前
11秒前
梁张鹏完成签到,获得积分10
12秒前
12秒前
12秒前
asfgsdag完成签到,获得积分10
12秒前
Kevin完成签到,获得积分10
12秒前
我想进步完成签到,获得积分10
12秒前
xmr发布了新的文献求助20
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
A Case Study on Hotels as Noncongregate Emergency Living Accommodations for Returning Citizens 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7765446
求助须知:如何正确求助?哪些是违规求助? 9309719
关于积分的说明 20312213
捐赠科研通 7350257
什么是DOI,文献DOI怎么找? 3314866
关于科研通互助平台的介绍 2464246
邀请新用户注册赠送积分活动 2329339