加强诊断决策:组合学习技术可用于可靠的压力水平分类
Raghav V Anand1, Abdul Quadir Md1, Shabana Urooj2
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India.
Diagnostics (Basel, Switzerland)
|November 24, 2023
概括
学术压力导致学生的压力,影响心理和身体健康. 这项研究引入了一个集体学习模型,使用睡眠和学习习惯等因素准确地分类学生的压力水平,达到93.48%的准确性.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 教育教育教育教育教育教育.
背景情况:
- 强烈的学术压力导致学生的压力,并可能产生不利的心理和身体影响.
- 转向在线学习增加了学生的工作量和压力水平.
- 学生可能会通过睡眠不足和饮食习惯的改变来表现出压力.
研究的目的:
- 提出一种新的集体学习架构,用于分类学生的压力水平.
- 确定导致学业压力的关键因素,包括睡眠,屏幕时间和学习习惯.
- 开发一个有效的模型来预测和管理学生的学术压力.
主要方法:
- 收集了学生关于睡眠时间,生产时间,屏幕时间,作业和学习方法的调查数据.
- 预先处理的数据将压力分为"高度压力"",可管理的压力"和"没有压力".
- 采用了针对少数阶级失衡的过量抽样,并实施了集体学习算法 (决策树,随机森林,AdaBoost,梯度提升).
主要成果:
- 集体学习模型实现了93.48%的准确性和93.14%的F1分数.
- 五次交叉验证的准确率为93.45%.
- 接收器运行特征 (ROC) 分析显示"没有压力"的准确率为98%,而"可控"和"高压力"类别的真实阳性率为91%.
结论:
- 拟议的集体学习方法与交叉验证准确地预测学生的压力水平,超越了最先进的算法.
- 这种模式可以帮助学生确定需要改进的领域,并减少学术压力.
- 这些发现支持开发有效的压力预测工具,以改善学术生活方式.
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