在高中青少年中发现与智能手机成相关的因素:使用机器学习和网络分析
Zhiyuan Li1, Qianmei Long2, Menghan Hao3
1The Third Hospital of Mianyang, Sichuan Mental Health Center, 621000 Mianyang, China; Department of Psychiatry, National Center for Mental Disorders, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, 410011 Changsha, China.
Journal of affective disorders
|August 23, 2025
概括
智能手机成是青少年日益关注的问题. 这项研究确定焦虑是高中生抗抑郁症的关键预测因素,为有针对性的干预提供了见解.
科学领域:
- 心理学
- 数据科学
- 青少年的健康
背景情况:
- 智能手机成是全球普遍存在的问题.
- 在高中人口中对SA预测因素的研究有限.
- 了解这些预测因素对于开发有效的干预措施至关重要.
研究的目的:
- 确定高中生中SA的主要预测因素.
- 探索这些预测因素之间的相互关系.
- 为青少年提供针对性SA干预的理论基础.
主要方法:
- 使用极度梯度提升 (XGBoost) 算法进行预测识别.
- 应用SHAP值来对20个候选变量的重要性进行排序.
- 使用网络分析可视化九大预测因素之间的关系.
- 其中包括14036名15至18岁的高中生接受模范培训和验证.
主要成果:
- 对于SA,XGBoost表现出优异的预测性能.
- 焦虑是SA最主要的预测因素.
- 焦虑与人际敏感性和情绪波动有积极的关联.
结论:
- 结合XGBoost和网络分析,有效地识别和探索青少年的SA预测因素.
- 鉴定到的因素,特别是焦虑,为基于证据的SA干预提供了宝贵的见解.
- 这种方法为为青少年提供有针对性的支持提供了基础.
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