基于机器学习构建青少年网络游戏成瘾的预测模型

孔维森; 王凯伦; 庹安写; 李兵; 郑曲波; 蒋怀滨

doi:10.16835/j.cnki.1000-9817.2024239

基于机器学习构建青少年网络游戏成瘾的预测模型

doi: 10.16835/j.cnki.1000-9817.2024239

1.
贵州医科大学公共卫生与健康学院, 贵阳 550004
2.
贵州医科大学医学人文学院
3.
贵州大学外语学院
4.
江西财经大学信息管理学院

基金项目:

贵州省卫生健康委2023年科学技术基金项目 gzwkj2023-476

详细信息

作者简介:
孔维森(1998-)，男，贵州贵阳人，在读硕士，主要研究方向为儿少卫生学

通讯作者:
庹安写，E-mail: 1298253638@qq.com

利益冲突声明 所有作者声明无利益冲突。
中图分类号: C913.5 B844.2 TB181 TN711.5
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-06
- 修回日期: 2024-07-03
- 刊出日期: 2024-08-25

Building a predictive model for adolescent Internet gaming disorder based on machine learning

1.
School of Public Health and Health Sciences, Guizhou Medical University, Guiyang (550004), Guizhou Province, China

摘要

摘要: 目的探索机器学习预测青少年网络游戏成瘾的效果，为制定有效的干预措施提供指导。方法于2023年6—9月，采用分层随机整群抽样方法选取贵州省毕节市、黔西市和金沙县3个地区3所初中和3所高中2 100名学生作为研究对象。采用简式网络游戏障碍量表(IGDS9-SF)、父母心理控制与自主支持问卷(PPCASQ)、动机结构问卷、相对剥夺感问卷、越轨同伴交往问卷以及自我控制双系统量表进行数据收集。描述性统计分析确定样本特征，使用χ²检验和Mann-Whitney U检验分析变量的组间差异。以人口学变量和各种影响因素作为自变量，以青少年是否网络游戏成瘾作为因变量，运用随机森林、逻辑回归、支持向量机、梯度提升树、决策树和自适应提升算法多种机器学习算法构建预测模型。结果青少年网络游戏成瘾检出率为4.57%(96名)；男生和初中生网络游戏成瘾检出率(5.52%，6.29%)相较女生和高中生(3.32%，3.62%)更高，差异均有统计学意义(χ²值分别为5.71，7.86，P值均 < 0.01)。网络游戏成瘾组相对剥夺感、越轨同伴交往、父亲心理控制、母亲心理控制、控制动机、冲动系统及其维度(冲动性、易分心、低延迟满足)得分高于非网络游戏成瘾组，而父母自主支持得分低于非网络游戏成瘾组(Z值分别为-2.88，-9.32，-4.13，-4.48，-6.58，-7.50，-7.18，-7.56，-7.43，-2.27，P值均＜0.05)。预测模型中，自适应提升算法表现最佳(精确度99%，召回率95%，F1分数97%，AUC值为0.96)；其次为随机森林和梯度提升树(精确度均为98%，召回率均为95%，F1分数分别为97%和96%，AUC值均为0.96)。结论相较于其他模型，自适应提升算法对青少年网络游戏成瘾有良好预测效果。应选择适合模型尽早识别存在网络游戏成瘾的个体，制定有效的干预策略，降低青少年网络游戏成瘾风险。
- 因特网 /
- 行为, 成瘾 /
- 精神卫生 /
- 模型, 统计学 /
- 青少年
Abstract: Objective To explore the effectiveness of machine learning in predicting adolescent Internet gaming disorder, so as to provide guidance for formulating effective intervention measures. Methods From June to September, 2023, a total of 2 100 students from 3 middle schools and 3 high schools in Bijie City, Qianxi City and Jinsha County, Guizhou Province were selected by stratified random cluster sampling as research subjects. Data was collected by using several instruments, including the Nine-item Internet Gaming Disorder Scale-Short From (IGDS9-SF), Parental Psychological Control and Autonomy Support Questionnaire(PPCASQ), Motivation Structure Questionnaire, Relative Deprivation Questionnaire, Deviant Peer Association Questionnaire, and Dual Systems of Self-control Scale. Descriptive statistical analysis was conducted to characterize the sample features, and the distribution differences of categorical variables were analyzed by using Chi-square test and Mann-Whitney U test. Demographic variables and various influencing factors were served as independent variables, and whether adolescents were addicted to Internet gaming was the dependent variable. Various machine learning algorithms, including random forest, Logistic regression, support vector machine, gradient boosting trees, decision trees, and adaptive boosting were employed to construct predictive models. Results The detection rate of Internet gaming disorder among adolescents was 4.57% (96 cases). Males and middle school students had higher Internet gaming disorder detection rates (5.52%, 6.29%) than females and high school students (3.32%, 3.62%), and the differences were statistically significant (χ²=5.71, 7.86, P < 0.01).The scores of relative deprivation, deviant peer affiliation, paternal psychological control, maternal psychological control, control motivation, impulsive system and its dimensions (impulsivity, distractibility, low delay of gratification) in Internet gaming disorder group were higher than in non-Internet gaming disorder, while the score of parental autonomy support was lower than that in the non-Internet gaming disorder group (Z=-2.88, -9.32, -4.13, -4.48, -6.58, -7.50, -7.18, -7.56, -7.43, -2.27, P < 0.05). The adaptive boosting algorithm performed the best (accuracy=99%, recall=95%, F1 score=97%, AUC=0.96). Random forest and gradient boosting trees also performed excellently (accuracy=98%, recall=95%, F1 score=97%, 96%, AUC=0.96). Conclusions Compared to other models, the adaptive boosting algorithm shows a good predictive effectiveness for adolescent Internet gaming disorder. Appropriate models should be selected to identify individuals with Internet gaming disorder as early as possible, to develop effective intervention strategies and reduce the risk of Internet gaming disorder.

注释:

1) 利益冲突声明 所有作者声明无利益冲突。

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图 1 青少年网络游戏成瘾影响因素重要性排序的自适应提升算法

Figure 1. Adaptive boosting algorithm illustration of the importance ranking of factors influencing adolescent Internet gaming disorder

下载: 全尺寸图片幻灯片

图 2 青少年网络游戏成瘾影响因素重要性排序的随机森林算法

Figure 2. Random forest illustration of the importance ranking of factors influencing adolescent Internet gaming disorder

下载: 全尺寸图片幻灯片

图 3 青少年网络游戏成瘾影响因素重要性排序梯度提升树算法

Figure 3. Gradient boosting tree illustration of the importance ranking of factors influencing adolescent Internet gaming disorder

下载: 全尺寸图片幻灯片

表 1 不同人口学特征青少年网络游戏成瘾检出比较

Table 1. Comparison of Internet gaming disorder detection rates among adolescents with different demographic variables

人口学指标	选项	人数	网络游戏成瘾人数	χ²值	P值
性别	男	1 196	66(5.52)	5.71	＜0.01
	女	904	30(3.32)
学段	初中	747	47(6.29)	7.86	＜0.01
	高中	1 353	49(3.62)
民族	汉族	1 419	57(4.02)	3.08	0.08
	其他	681	39(5.73)
户籍类型	城镇	340	16(4.71)	0.17	0.89
	农村	1 760	80(4.55)
是否独生子女	是	297	15(5.05)	0.18	0.67
	否	1 803	81(4.49)
是否留守	是	350	22(6.29)	2.89	0.09
	否	1 750	74(4.23)
家庭类型	完整	1 557	67(4.30)	0.99	0.31
	其他	543	29(5.34)
母亲文化程度	初中及以下	1 740	71(4.08)	4.40	0.11
	高中/中专	252	14(5.56)
	大专及以上	108	11(10.19)
父亲文化程度	初中及以下	1 697	77(4.54)	3.06	0.21
	高中/中专	283	10(3.53)
	大专及以上	120	9(7.50)
注：()内数字为检出率/%。

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表 2 不同网络游戏成瘾组别青少年各量表得分比较[M(P₂₅，P₇₅)]

Table 2. Comparison of scores among adolescents with different Internet gaming disorder groups[M(P₂₅, P₇₅)]

网络游戏成瘾	人数	越轨同伴交往	父母自主支持	相对剥夺感	母亲心理控制	父亲心理控制	控制动机	冲动性	易分心	低延迟满足	问题解决	未来时间观	控制系统	冲动系统
是	96	18(14, 22)	22(16, 30)	10(7, 12)	49(36, 65)	42(28, 56)	28(21, 39)	15(10, 20)	11(8, 13)	9(6, 11)	18(15, 24)	7(5, 11)	26(20, 34)	52(46, 61)
否	2 004	12(9, 16)	25(19, 31)	9(6, 11)	39(29, 51)	34(22, 45)	20(16, 26)	10(7, 13)	7(5, 9)	6(3, 8)	20(16, 24)	8(6, 10)	28(23, 33)	44(38, 50)
合计	2 100	12(9, 16)	25(19, 31)	9(6, 11)	40(29, 51)	35(23, 46)	20(16, 27)	10(7, 14)	8(5, 10)	6(4, 8)	20(16, 24)	8(6, 10)	28(23, 33)	44(38, 50)
Z值		-9.32	-2.27	-2.88	-4.48	-4.13	-6.58	-7.18	-7.56	-7.43	-0.79	-0.99	-0.91	-7.50
P值		＜0.01	0.02	＜0.01	＜0.01	＜0.01	＜0.01	＜0.01	＜0.01	＜0.01	0.42	0.32	0.35	＜0.01

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表 3 不同机器学习算法预测青少年网络游戏成瘾效果

Table 3. Performance of different machine learning algorithms in predicting adolescent Inetrnet gaming disorder

机器学习算法	精确度/%	召回率/%	F1/%	AUC值
支持向量机	98	88	93	0.84
决策树	92	92	92	0.84
梯度提升树	98	95	96	0.96
自适应提升算法	99	95	97	0.96
随机森林	98	95	97	0.96
逻辑回归	95	88	91	0.88

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