首页|基于机器学习算法的青少年电子烟使用及影响因素分析

基于机器学习算法的青少年电子烟使用及影响因素分析

Analysis of adolescent e-cigarette use and influencing factors based on machine learning algo-rithms

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  • 维普
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目的:了解广西某市15岁以上青少年吸电子烟现状及影响因素,为控制电子烟在青少年中的流行提供资料参考.方法:通过多阶段分层整群随机抽样对广西某市15岁以上青少年进行问卷调查,综合运用logistic回归、随机森林、XGboost、支持向量机模型、单隐藏层神经网络、KNN模型进行影响因素分析.结果:广西某市15岁以上青少年电子烟使用率为1.68%,其中高中生、职高生电子烟使用率分别为1.08%、1.74%;不同的机器学习模型在各项评价指标的表现上各有优劣;青少年使用电子烟的9个主要影响因素包括:过去 30d是否在互联网上看到电子烟广告、朋友是否吸烟、学习压力水平、是否看到过老师吸烟、抑郁情况、性别、公共场合是否看到有人吸烟、吸烟是否使年轻人具有吸引力、是否有人给免费烟草产品.结论:广西某市15岁以上青少年电子烟使用率相对较低,可将6种机器学习模型的结果结合起来对青少年电子烟使用行为进行预测,判断使用人群的特征.
Objective:To understand the current situation of e-cigarette use and influencing factors among ado-lescents aged 15 and above in a certain city in Guangxi in order to provide data and references for controlling the prevalence of e-cigarettes among adolescents.Methods:A questionnaire survey was conducted among adoles-cents aged 15 and above in a certain city in Guangxi through multi-stage stratified cluster random sampling.Lo-gistic regression,random forest,XGboost,support vector machine models,single hidden layer neural networks,and KNN models were applied comprehensively for the analysis of influencing factors.Results:The prevalence of e-cigarette use among adolescents aged 15 and above in a certain city in Guangxi was 1.68%,with the usage rates among high school and vocational high school students being 1.08%and 1.74%,respectively.Different ma-chine learning models demonstrated varying levels of performance across evaluation metrics.Nine primary influ-encing factors were identified for adolescent e-cigarette use:exposure to e-cigarette advertisements on the inter-net in the past 30 days,friends'smoking habits,level of academic pressure,exposure to teachers smoking,de-pression status,gender,exposure to smoking in public places,perception of smoking as enhancing attractiveness among youth,and receiving free tobacco products.Conclusion:The prevalence of e-cigarette use among adoles-cents aged 15 and above in the city is relatively low.It is possible to combine the results of six machine learning models to predict adolescent electronic cigarette usage behavior and identify the characteristics of the user popula-tion.

adolescentse-cigarettesmachine learninglogistic regression modelrandom forest modelXG-boost modelsupport vector machine modelsingle hidden layer neural network modelKNN model

徐心怡、朱平华、罗娜、蒋碧玲、张秀岚、白思怡、王宣伊、黄靖语、刘苏仪、潘怡双、谭琼

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广西医科大学人文社会科学学院,南宁 530021

广西壮族自治区南宁市卫生健康委员会,南宁 530021

青少年 电子烟 机器学习 logistic回归模型 随机森林模型 XGboost模型 支持向量机模型 单隐藏层神经网络模型 KNN模型

广西高校人文社会科学重点研究基地(健康与经济社会发展研究中心)课题资助项目南宁市疾病预防控制中心科研项目

2022RWB15Z20211227

2024

10.16190/j.cnki.45-1211/r.2024.01.017

广西医科大学学报
广西医科大学

广西医科大学学报

CSTPCD
影响因子：0.788
ISSN：1005-930X
年,卷(期)：2024.41(1)
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