Prediction of Stress Level on Indian Working Professionals Using Machine Learning

被引:0
|
作者
Pabreja, Kavita [1 ]
Singh, Anubhuti [2 ]
Singh, Rishabh [2 ]
Agnihotri, Rishita [2 ]
Kaushik, Shriam [3 ]
Malhotra, Tanvi [2 ]
机构
[1] GGSIP Univ, Maharaja Surajmal Inst, Delhi, India
[2] Deloitte, Bengaluru, India
[3] Prague Univ Econ & Business, Prague, Czech Republic
关键词
Data Transformation; Data Visualization; Explanatory Data Analysis; Exploratory Data Analysis; Feature Selection; Random Forest Regressor; Stress; Supervised Learning; Support Vector Machine;
D O I
10.4018/IJHCITP.297077
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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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页数:26
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