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학술대회 Random Forest Approach in Prediction Workers’ Stress from Personality Traits
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저자
김정숙, 윤대섭, 김현숙
발행일
202210
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1-3
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952879
협약과제
21PR4900, 지식근로자 대상 인공지능 기반 멘탈 웰빙/헬스 관리 솔루션 개발(19PR2600 이월), 김현숙
초록
Continuous exposure to stress threatens the physical and mental health of workers and reduces their quality of life. On the business side, stress has negative consequences, such as reduced productivity. Therefore, it needs to assess and manage stress. In this study, we assess stress using workers' personality traits collected from online surveys. Stress was measured by the VAS tool and classified by stress and normal groups. As a result of the experiment, the Random Forest model with the personality traits as input features was able to classify stress with an accuracy of 81 %. It was feasible to improve the accuracy by 90% by adding dynamic factors.
KSP 제안 키워드
Input features, Quality of life, dynamic factors, mental health, online survey, personality traits, random forest model