College of Nursing, Ewha Womans University, Seoul, Republic of Korea.
Department of Medical Life Sciences, School of Medicine, The Catholic University of Korea, Seoul, South Korea.
Front Public Health. 2023 Jul 12;11:1201054. doi: 10.3389/fpubh.2023.1201054. eCollection 2023.
The incidence of depression among employees has gradually risen. Previous studies have focused on predicting the risk of depression, but most studies were conducted using basic statistical methods. This study used machine learning algorithms to build models that detect and identify the important factors associated with depression in the workplace.
A total of 503 employees completed an online survey that included questionnaires on general characteristics, physical health, job-related factors, psychosocial protective, and risk factors in the workplace. The dataset contained 27 predictor variables and one dependent variable which referred to the status of employees (normal or at the risk of depression). The prediction accuracy of three machine learning models using sparse logistic regression, support vector machine, and random forest was compared with the accuracy, precision, sensitivity, specificity, and AUC. Additionally, the important factors identified sparse logistic regression and random forest.
All machine learning models demonstrated similar results, with the lowest accuracy obtained from sparse logistic regression and support vector machine (86.8%) and the highest accuracy from random forest (88.7%). The important factors identified in this study were gender, physical health, job, psychosocial protective factors, and psychosocial risk and protective factors in the workplace.
The results of this study indicated the potential of machine learning models to accurately predict the risk of depression among employees. The identified factors that influence the risk of depression can contribute to the development of intelligent mental healthcare systems that can detect early signs of depressive symptoms in the workplace.
员工群体中的抑郁发生率逐渐升高。既往研究多聚焦于预测抑郁风险，然而大部分研究采用的是基本的统计学方法。本研究使用机器学习算法构建模型，以识别和检测工作场所中与抑郁相关的重要因素。
共有 503 名员工完成了一项在线调查，调查内容包括一般特征、身体健康、工作相关因素、心理社会保护因素和工作场所风险因素的问卷。数据集包含 27 个预测变量和一个因变量，因变量表示员工的状态（正常或处于抑郁风险中）。比较了稀疏逻辑回归、支持向量机和随机森林三种机器学习模型的预测准确性，评估指标包括准确性、精密度、敏感度、特异度和 AUC。此外，还确定了稀疏逻辑回归和随机森林中识别的重要因素。
所有机器学习模型的结果相似，稀疏逻辑回归和支持向量机的准确性最低（86.8%），随机森林的准确性最高（88.7%）。本研究确定的重要因素包括性别、身体健康、工作、心理社会保护因素以及工作场所的心理社会风险和保护因素。
研究结果表明，机器学习模型有潜力准确预测员工的抑郁风险。确定的影响抑郁风险的因素有助于开发智能心理健康保健系统，以便在工作场所中检测到抑郁症状的早期迹象。
