Prediction of Stress Level on Indian Working Professionals Using Machine Learning

Kavita Pabreja, Anubhuti Singh, Rishabh Singh, Rishita Agnihotri, Shriam Kaushik, T. Malhotra
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引用次数: 2

Abstract

Stress levels amongst the Indian employees have increased due to a variety of factors and are a matter of great concern for organizations. This study is based on Indian working professionals and real data has been collected by using non-probability convenience sampling. A questionnaire was drafted based on 18 factors affecting the mental health of professionals. This study addresses two dimensions. The first is to identify the important influential features that trigger stress in the lives of working professionals, and the second is to predict the stress levels. Various supervised machine learning algorithms have been experimented with, and of all these algorithms, the support vector machine regressor model showed the best performance. The main contribution of the paper lies in the identification and ranking of 10 important stress triggering features that can guide organizations to develop policies to take care of their employees. The other deliverable is the development of a GUI-based stress prediction software based on machine learning techniques.
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使用机器学习预测印度工作专业人员的压力水平
由于各种因素,印度员工的压力水平有所增加,这是组织非常关注的问题。本研究以印度在职专业人士为研究对象,采用非概率方便抽样的方法收集真实数据。根据影响专业人员心理健康的18个因素起草了一份调查问卷。本研究涉及两个维度。首先是确定在职业人士的生活中触发压力的重要影响特征,其次是预测压力水平。各种监督机器学习算法已经被实验过,在所有这些算法中,支持向量机回归模型表现出最好的性能。本文的主要贡献在于识别并排名了10个重要的压力触发特征,这些特征可以指导组织制定照顾员工的政策。另一个成果是基于机器学习技术的基于gui的应力预测软件的开发。
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来源期刊
CiteScore
4.10
自引率
36.80%
发文量
30
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