在大学生中使用机器学习识别抑郁症和积极/消极情绪变化的风险因素
Qi Qiang1, Jinsheng Hu1, Xianke Chen1
1Department of Psychology, Liaoning Normal University, Dalian, China.
Frontiers in public health
|July 24, 2025
概括
机器学习准确地预测了大学生抑郁症的变化. 基线抑郁症和父母的情绪表达是积极和消极情绪转变的关键预测因素.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 心理健康 心理健康
背景情况:
- 大学生面临着重大的心理健康挑战,包括抑郁症.
- 了解抑郁症变化的预测因素对于及时干预至关重要.
研究的目的:
- 应用机器学习模型来预测大学生抑郁症变化的程度.
- 确定影响抑郁症波动的关键心理变量.
主要方法:
- 收集了大学生关于抑郁症,人口统计,育儿风格,心理健康,个性,应对,SCL-90和社会支持的数据.
- 使用后勤回归,随机森林,支持向量机 (SVM) 和k-最近邻近算法.
- 选择了表现最好的模型并分析了功能的重要性.
主要成果:
- 支持矢量机 (SVM) 显示出卓越的性能,在预测负面抑郁变化方面达到89.4%的准确性,在预测正面变化方面达到91.9%.
- 基线抑郁水平,父亲的情绪表达和母亲的情绪表达被确定为重要的预测因素.
- 这些因素对于预测抑郁症的增加和减少都很重要.
结论:
- 机器学习模型有效地预测了大学生抑郁症变化的程度.
- 父母的情绪表达和最初的抑郁水平是预测抑郁轨迹的关键因素.
- 这些发现为心理健康研究和临床实践提供了新的方法.
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