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机器学习研究预测黎巴嫩校园中的焦虑

Madhuri Mahalingam¹, Manar Jammal², Reem Hoteit³

¹SSN College of Engineering, Chennai, Tamilnadu, India.

Studies in health technology and informatics

|June 30, 2023

概括

大学学生面临着全球心理健康危机,COVID-19加剧了这种危机. 机器学习准确地预测了焦虑症状,自我评估的健康是关键指标.

科学领域:

心理健康研究心理健康研究
机器学习应用机器学习应用
高等教育研究高等教育研究

背景情况:

全球大学生正经历着严重的心理健康危机.
由于COVID-19的流行,学生们面临的心理健康挑战加剧了.
了解和预测学生的焦虑对于及时干预至关重要.

研究的目的:

为了调查黎巴嫩大学生之间的心理健康挑战.
开发和评估用于预测焦虑症状的机器学习模型.
确定学生群体中焦虑的关键预测因素.

主要方法:

在黎巴嫩的329名大学生中进行了一项调查.
使用了机器学习算法 (逻辑回归,MLP,SVM,RF,XGBoost).
模型使用人口统计和自我评估健康数据预测了焦虑症状.

主要成果:

多层感知器 (MLP) 模型实现了最高的性能,AUC得分为80.70%.
自我评估的健康状况成为焦虑症状最重要的预测因素.
该研究确定了与学生焦虑相关的关键人口统计和健康相关因素.

结论:

机器学习,特别是MLP,在预测学生焦虑方面表现出很高的有效性.
自我评估的健康是评估和预测学生心理健康的一个关键因素.
建议进行进一步的研究,包括数据增强和多类预测.

关键词:

机器学习是机器学习.焦虑是一种焦虑.抑郁抑郁症抑郁症抑郁症是一种心理健康心理健康大学的学生大学生.

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