Risk Modeling and Association Pathways Integrating Psychosocial Factors for Drug Misuse in Adolescents and Young Adults: A Machine Learning Approach

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Authors: Zhizhen Li, Na Li, Ying He, Tingyong Zhang, Anyong Peng, Zhongzhou Jiang, Cong Wen, Zhengcong Wu, Joyce Oi Kwan Chung, William Ho Cheung Li, Yu Cheng, Wei Xia
Cited by: 1
References: 37

Abstract

Introduction: Early identification for preventing drug misuse among adolescents and young adults (AYAs) is more cost-effective than drug treatment. However, there is a lack of scientific and comprehensive risk models for early identification. This study aimed to construct risk models and association pathways that integrate psychosocial factors influencing drug misuse in AYAs using a machine learning approach. METHODS: This cross-sectional study included 1,012 Chinese AYAs aged 14-35 years and was conducted from June to December 2023. Key psychosocial factors for drug misuse were identified using the least absolute shrinkage and selection operator and Boruta. A voting classifier combining logistic regression, K-nearest neighbors, support vector machine, and 5-fold cross-validation was used for model training and bootstrapping method wan applied for testing. Shapley additive explanation was used to determine the importance of each risk factor. A Bayesian network (BN) model was constructed to explore possible association pathways for drug misuse. RESULTS: A total of 3.85% of participants reported drug misuse. Significant risk factors included environmental exposure to drug misusers, working in nightlife venues (WNVs), cognition of traditional drug types, perceived risks of drug misuse, permissive attitude toward drug misuse, refusal self-efficacy of drug misuse (RSEDM), externalizing problem behavior, sensation-seeking, and adverse childhood experiences (ACEs). The voting model showed strong performance (recall: 94.10%; area under the curve: 98.11%; accuracy: 92.63%; precision: 34.15%; F-score: 49.68%). The BN model revealed that ACEs played a central role in risk assessment of drug misuse. AYAs WNVs, with low RSEDM and exposure to drug misusers, had exceeding 90% probability of drug misuse. CONCLUSION: ACEs serve as a primary risk factor, with a significant impact on social environmental influences. AYAs with ACEs and those WNVs require targeted and early interventions to prevent drug misuse. .

Fields of Study & Subjects

Opioid Use Disorder Treatment Substance Abuse Treatment and Outcomes HIV Drug Use Sexual Risk

Publication details

Journal / source
European Addiction Research
Publisher
S. Karger AG
Volume
32
Issue
2
Pages
105–119
Publication date
21 February 2026
Language
EN