IT EMPLOYEE STRESS PREDICTION BY USING MACHINE LEARNING AND COMPUTER VISION TECHNIQUE

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Reeja S R
Lavanya S
Monish L
Abdul Basit Lone

Abstract

Stress assessment must be performed in early stages because stress related issues tend to rise in depression, heart attacks and strokes. Stress leads to have a greater impact on the thoughts sometimes provokes suicidal attempts within employees. The machine learning techniques has proved to be extensive for medical analysis and prediction. This approach can further be used with neurological tools. The 2017 OSMI mental health survey dataset represents working professionals within the IT company and this dataset is used for classifying the stressed or unstressed employees. After data cleaning and pre-processing further training of model is performed by using machine learning techniques. The accuracy is predicted for both the models. Among those models boosting has given highest accuracy.

 

 

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References

U Srinivasulu Reddy, Aditya vivek Thota, Adharun,†Machine learning Techniques for Stress prediction in working employee†2018 IEEE International Conference on computational intelligence and computing research.

G. Mikelsons, M. Smith, A. Mehrotra, M. Musolesi, “Towards deep learning models for psychological state prediction using smartphone data: Challenges and opportunities, 2017.

Samrat Bisai, Richa Chaudhary, “Stress Among the Students of an Engineering Institution in India: An Empirical Analysisâ€, Jindal Journal of Business Research, Volume: 6 issue: 2, page(s): 186-198 Article first published online: October 30, 2017; Issue published: December 1, 2017

Huijie Lin, Jia Jia, Quan Guo, Yuanyuan Xue, “User-level psychological stress detection from social media using deep neural networkâ€, 2014, ACM, New York, NY, USA, 507-516

C. Liao, R. Chen, and S. Tai, Emotion stress detection using EEG signal and deep learning technologies - IEEE Conference Publication, 2018 IEEE Int. Conf. Appl. Syst. Invent., no. 2, pp. 9093, 2018

Chigerwe et al. BMC Medical Education 2014, Published on: 28 November 2014

Gender and Stress. (n.d.). Retrieved from APA press release 2010.

A.P. Singh and H.C. Singh, “Occupational Stress, Security and Insecurity with Job Involvement of First Level Industrial Supervisorsâ€, Indian Journal of Industrial Relations, Vol. 20, No. 2, 1984, pp. 177-183

T.R. Rajeswari, “Employee Stress: A Study with Reference to Bank Employeesâ€, Indian Journal of Industrial Relations, Vol. 27, No. 4, 1992, pp. 419-425

Ashwinkumar.U.M and Dr. Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012

Takashi Shimizu and Shoji Nagata, “Relationship between Job Stress and Self Rated Health among Japanese Full-Time Occupational Physiciansâ€, Environmental Health and Preventive Medicine, Vol. 10, 2005, pp. 227-232.

Jay P. Mulki, Jorge F. Jaramillo and William B. Locander, “Effect of Ethical Climate on Turnover Intention: Linking Attitudinal and Stress Theoryâ€, Journal of Business Ethics, Vol. 78, 2008, pp. 559-574.