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首页> 外文会议>IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering >Predicting Mental health disorders using Machine Learning for employees in technical and non-technical companies
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Predicting Mental health disorders using Machine Learning for employees in technical and non-technical companies

机译：预测技术和非技术公司员工机器学习的心理健康障碍

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摘要

mental health has always been an important and challenging issue, especially in the case of working Professionals. The modernized (hectic) lifestyle and workload take a toll over people over time making them more prone to mental disorders like mood disorder and anxiety disorder. Thus, the risk mental health problems increase in working professionals. To deal with this problem industries provide mental health care incentives to their employees, but it is not enough to deal with the problem. In this paper, we utilize the data from mental health survey 2019 that contains the data of working professionals for both tech and non-tech company employees. We process data to find the features influencing the mental health of employees or features that can help to predict the mental health of the employee the feature can be either personal or professional. We apply multiple machine learning algorithms to find the model with the best accuracy. We take precision and recall as the measure to check the performance of different ML models.
机译：心理健康一直是一个重要而挑战性的问题，特别是在工作专业人士的情况下。现代化的（忙碌的）生活方式和工作量随着时间的推移，让他们更容易发生心理障碍和焦虑症。因此，工作专业人员的风险心理健康问题增加。要处理这个问题，行业为员工提供精神保健激励，但不足以处理这个问题。在本文中，我们利用2019年的心理健康调查数据，其中包含技术和非技术公司员工的工作专业人员数据。我们处理数据以查找影响员工心理健康或功能的功能，这些功能可以有助于预测员工的心理健康，该功能可以是个人或专业的。我们应用多种机器学习算法，以最佳精度找到模型。我们采用精度并召回作为检查不同ML型号的性能的措施。

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