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

Reeja S R; Lavanya S; Monish L; Abdul Basit Lone

doi:10.26483/ijarcs.v12i0.6728

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

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.

Keywords

Stress, SVM, Random forest classifier, Mental disorder

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References

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DOI: https://doi.org/10.26483/ijarcs.v12i0.6728

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