分析大学生的心理和社会压力:一种机器学习方法
Ishrak Jahan Ratul1, Mirza Muntasir Nishat1, Fahim Faisal1
1Department of EEE, Islamic University of Technology, Gazipur, Bangladesh.
Heliyon
|June 19, 2023
概括
机器学习模型准确地预测了大学生高度的心理和社会压力,超过24%的学生经历了极端的心理压力. 早期发现有助于学业成功和幸福.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 由于孤立,数字依赖和减少社交活动,COVID-19大流行显著增加了大学生之间的心理和社会压力.
- 早期发现学生的压力对于学业成绩和心理健康至关重要.
- 机器学习 (ML) 为早期压力预测和干预提供了一个有希望的方法.
研究的目的:
- 开发和验证可靠的基于机器学习的预测模型,用于大学生感知到的压力.
- 为了确定学生群体中高度社会和心理压力的普遍性.
- 评估不同的ML算法和特征减小技术的有效性,以预测压力.
主要方法:
- 使用监督机器学习方法,通过在线调查从444名大学生收集数据.
- 特性减少技术包括主要组件分析 (PCA) 和千二测试.
- 使用网格搜索交叉验证 (GSCV) 和遗传算法 (GA) 进行了超参数优化 (HPO).
主要成果:
- 11.26%的学生报告高社会压力,24.10%报告极高的心理压力.
- 开发的ML模型实现了高预测性能,多层感知器模型与PCA和GSCV相结合显示了最高的准确性 (80.5%).
- 关键性能指标包括准确度 (80.5%),精度 (1.000),F1得分 (0.890) 和回忆 (0.826).
结论:
- 机器学习模型可以有效地预测大学生的感知压力水平.
- 这些发现突出了心理压力的严重流行,需要有针对性的干预措施.
- 这项研究表明,基于机器学习的策略有可能在压力时期支持学生的福祉.
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