Background: Nuclear accidents often result in serious human, economic and environmental damage, and human error can be one of the causes of such accidents. Employees in any organization are one of the most important factors that play a role in this and their performance can be affected by their personality traits.
Purpose: The purpose of the research is to know the role of performance-shaping factors and the role of work behavior patterns in human error, as well as to determine the probability of each error with the origin of work behavior patterns and to provide a solution to prevent errors with the origin of work behavior patterns.
Methodology: This research is applied in terms of purpose and in terms of the method of data collection from two library and field methods. In this research, the approach of machine learning algorithms has been used.
Findings: Detecting and reducing human error in nuclear power plants is a critical research area, because human errors can have serious consequences for both safety and efficiency of nuclear power plants. Human reliability in complex systems is influenced by various performance shaping factors. Despite all the technological advances, the human factor still has the largest share in the occurrence of accidents, with the establishment of the behavioral model of reducing human error, an important part of the factors of error occurrence will be under control. Therefore, identifying the factors related to human error and suggestions to reduce it has a significant impact on the organization's performance and is one of the most important issues of senior management.
Conclusion: The differences and characteristics of personality types can affect human performance. Paying attention to these differences and abilities can help improve group interactions and reduce human error in work environments. In this research, with the help of machine learning algorithms, the effect of personality type on human error and solutions to reduce it wer