基于机器学习的韩国青少年自杀风险预测
Haitao Wang1, Han Yuan1, Yunong Zhang2
1Department of Physical Education, Kyungpook National University, Daegu, 41566, Republic of Korea.
Scientific reports
|April 28, 2025
概括
机器学习模型使用国家调查数据准确预测青少年的自杀行为. 压力和抑郁是关键的危险因素,突出了早期干预策略的必要性.
科学领域:
- 精神病学和计算科学 精神病学和计算科学
- 青少年心理健康研究 青少年心理健康研究
背景情况:
- 传统的自杀行为风险评估工具是不够的.
- 机器学习 (ML) 越来越多地被用于心理护理中的风险预测.
研究的目的:
- 通过使用国家调查数据,评估ML模型来预测青少年的自杀行为.
- 为了比较六种不同的ML模型的性能.
主要方法:
- 六个ML模型 (LR,DT,SVM,GBM,ET,DRF) 进行了比较.
- 为了解释性,使用了夏普利添加式扩展 (SHAP) 和变特征重要性 (PFI).
- 相互作用分析检查了变量关系.
主要成果:
- 梯度增强机 (GBM) 模型显示了最高的预测准确度 (88%).
- 确定的主要预测因素是压力和抑郁症.
- 较低的焦虑与较高抑郁水平的自杀风险降低相关.
结论:
- 将ML与国家调查数据相结合,改善了青少年自杀风险预测.
- 这些发现支持针对处于危险的年轻人的早期干预策略.
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