机器学习在预测青少年互联网行为成方面的应用
Yao Gan1, Li Kuang1, Xiao-Ming Xu1
1Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Frontiers in psychiatry
|April 16, 2025
概括
男性青少年,城市居民以及冲动性和神经性等特定心理特征是互联网成的关键风险因素. 机器学习模型,特别是极端梯度增强,可以预测这种行为,帮助有针对性的干预.
科学领域:
- 青少年心理学 青少年心理学
- 行为科学是一种行为科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 互联网成是青少年日益关注的问题.
- 识别风险因素对于制定有效的预防策略至关重要.
研究的目的:
- 探索与青少年互联网成相关的风险因素.
- 开发和评估用于预测互联网成的机器学习模型.
主要方法:
- 在重庆4461名高中生中进行了分层集群抽样.
- 后勤回归分析以确定独立的风险因素.
- 用于预测的六种机器学习模型 (MLP,RF,KNN,SVM,LR,XGBoost) 的比较.
主要成果:
- 男性性别,城市居住和特定的心理特征 (例如精神病症,神经病症,抑郁症) 是显著的危险因素.
- 极端梯度增强 (XGBoost) 在评估模型中显示出最高的预测性能 (AUC=0.836).
- 机器学习模型在预测青少年互联网成方面表现整体中等.
结论:
- 青少年的互联网成与男性性别,城市生活和某些性格特征有关.
- 机器学习,特别是XGBoost,为预测和管理青少年互联网成提供了一个有希望的方法.
- 调查结果可以为处于危险的青少年提供有针对性的干预措施.
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