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预测机器学习模型用于评估黎巴嫩大学生在COVID-19期间的抑郁,焦虑和压力

Christo El Morr¹, Manar Jammal¹, Imad Bou-Hamad²

¹York University, Toronto, ON, Canada.

Journal of primary care & community health

|March 28, 2024

概括

大学学生面临着心理健康危机,COVID-19加剧了这种危机. 机器学习模型有效地使用人口统计和自我评估健康数据预测抑郁和焦虑症状.

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
公共卫生公共卫生

背景情况:

全球大学生都面临着重大的心理健康挑战.
由于COVID-19的流行,学生群体的心理困境加剧了.
黎巴嫩大学面临着影响学生福祉的独特压力.

研究的目的:

评估黎巴嫩大学生心理健康状况.
开发和比较用于预测抑郁,焦虑和压力的机器学习模型.
确定这些疾病的关键人口统计和自我评估健康预测因素.

主要方法:

从黎巴嫩两所大学的学生那里收集的调查数据.
开发和评估了八种机器学习模型 (逻辑回归,MLP,SVM,RF,XGBoost,AdaBoost,NB,KNN).
使用曲线下的面积 (AUC) 度量表的性能比较.

主要成果:

随机森林 (RF) 显示了预测抑郁症的最高AUC (78.27%).
原始贝叶斯 (NB) 获得了焦虑预测的最高AUC (76.37%).
在压力预测方面,AdaBoost表现出最高的AUC (72.96%).
自评健康是抑郁症的关键预测因素;年龄和自评健康对焦虑和压力至关重要.

结论:

机器学习模型可以有效地预测大学生心理健康症状.
像RF,NB和AdaBoost这样的特定模型显示出确定有风险的学生的希望.
人口因素 (年龄) 和自我感知健康是学生心理健康的重要指标.

关键词:

焦虑是一种焦虑.抑郁抑郁症抑郁症抑郁症是一种机器学习是机器学习.心理健康心理健康压力就是压力,压力就是压力.大学的学生大学生.

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