Occupational stress is recognized as one of the major factors leads to health problems. This will lower efficiency and productivity on the job in an organization so the assessment and management of work-related stress are very crucial. Several kinds of computational techniques have been used for modeling and prediction but currently, occupational stress prediction in an early stage is still a challenge. This study is based on secondary data. In this paper, a review analysis has been carried out to analyze what has been done so far in last 26 years related to occupational stress and where there is a need to carry the further research. The paper explores occupational stress evaluation modeling techniques related to Machine Learning as well as statistical method. Occupational stress and burnout related to different kind of sector for working professional reviewed. This survey reviewed the subjective as well as objective measurement of stress evaluation. Questionnaires and physiological sensors used to measure and evaluate stress and corresponding techniques for modeling occupational stress have been reviewed. Occupational stress modeling based on Machine learning techniques such as ANN, BN, and SVM, LDA, RSM statistical methods like Regression, MLR etc. reviewed. This survey concludes with a discussion and future work, summary and finally conclusion.

Stress, Machine Learning Techniques, Stress Questionnaire, Stress Sensor, Stress classification, Stress prediction, Computational stress model

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