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

KONG Weisen; WANG Kailun; TUO Anxie; LI Bing; ZHENG Qubo; JIANG Huaibin

doi:10.16835/j.cnki.1000-9817.2024239

Volume 45 Issue 8

Aug. 2024

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KONG Weisen, WANG Kailun, TUO Anxie, LI Bing, ZHENG Qubo, JIANG Huaibin. Building a predictive model for adolescent Internet gaming disorder based on machine learning[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2024, 45(8): 1080-1085. doi: 10.16835/j.cnki.1000-9817.2024239

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KONG Weisen, WANG Kailun, TUO Anxie, LI Bing, ZHENG Qubo, JIANG Huaibin. Building a predictive model for adolescent Internet gaming disorder based on machine learning[J]. CHINESE JOURNAL OF SCHOOL HEALTH, 2024, 45(8): 1080-1085. doi: 10.16835/j.cnki.1000-9817.2024239

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

doi: 10.16835/j.cnki.1000-9817.2024239

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

Received Date: 2023-12-06
Rev Recd Date: 2024-07-03
Publish Date: 2024-08-25

Abstract

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.

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