Analysis of College Students' Mental Health Based on XGBoost
Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v7i2.3487
Abstract
With the rapid development of social economy and the change of students' growth environment, students' mental health problems have become increasingly prominent. In order to comprehensively and systematically understand the psychological stress of college students. In this paper, data mining technology and machine learning methods are used to intelligently evaluate the mental health of college students. By collecting relevant data and combining with SCL-90 mental health evaluation system, the machine learning algorithm is used to accurately simplify the questionnaire questions of the original evaluation system. And to determine the contribution of different indicators to the psychological stress of college students. Based on the simplified data, a psychological stress evaluation model for college students is established to realize the classification evaluation of mental health status. And provide a specific evaluation score or grade. Improve the accuracy of assessment through model training and optimization, and provide data support for follow-up mental health work. In addition, Kendall's Tau-B correlation analysis and XGBoost are used for feature selection, and key feature factors are retained. The method of this paper is helpful to accurately locate the psychological stress of college students, and provide a clear direction and basis for psychological intervention.
Keywords
student mental health, data mining, scl-90 scale, XGBoost model
Funding
Guangdong Provincial Education Science Planning Project under grant 2022GXJK378, Project of 2024 National University Student Innovation Training Program. (No. 202412621002).
Full Text
PDF - Viewed/Downloaded: 0 TimesReferences
[1] Xu Jie, Chen Yongyong, Hu Yongkang. Research on Automatic Rating Method of College Students' Mental Health Based on GA + BP Model [J]. Education Watch, 2022, 11(32): 18-22+37.
[2] Hao Wanlin. Research on Gray Correlation Algorithm of Factors for Mental Health Warning of College Students [J]. Electronic Design Engineering, 2022, 30(11): 12-16.
[3] Liu Jinming. Simplification and Application of SCL-90 Based on Machine Learning [D]. Qingdao University, 2020. 002011.
[4] Fan Wenrong. (2023) Research on Early Warning Model of College Students' Depression Tendency Based on Social Platform Data [D]. Nanjing University of Posts and Telecommunications.
[5] Zhu Lingyan. (2023) Data Analysis and Prediction of College Students' Mental Health Based on Data Mining [D]. Nanchang University.
[6] Gu Ronglong, Zhao Wenjie, Wang Lei. Application of data mining technology in the era of big data [J]. Science and Technology Innovation and Application, 2022, 12(5): 176-178.
[7] He Feng, Xing Lina, Ren Xuepu, et al. Investigation on mental health status of neurology practitioners in some areas of Hebei Province based on machine learning [J]. Journal of Brain and Neurological Diseases, 2024, 32(04): 240-246.
[8] Yang Juan. Research on the Application of Data Mining Technology in Predicting Mental Health Problems of Higher Vocational Students [J]. Science Consulting (Science and Technology · Management), 2021, (03): 161-162.
[9] Zhou Chengyi, Wang Yixin, Li Xiaoyuan. Intervention Model of College Students' Psychological Problems Based on Big Data [J]. Modern Communication, 2021, (17): 71-73.
[10] Hao Wanlin. Research on Gray Correlation Algorithm of Factors for Mental Health Warning of College Students [J]. Electronic Design Engineering, 2022, 30(11): 12-16.
[11] Tian Wei. Classification Algorithm Application of Big Data Mining — — Taking XGBoost as an Example [J]. Wireless Internet Technology, 2022, 19(19): 120-123.
[12] Mahendra A R P ,Irzam K R ,Sidharta S . Technique of Mental Health Issues Classification based on Machine Learning: Systematic Literature Review[J]. Procedia Computer Science, 2023, 227: 137-146.
[13] Wang Zhengli. (2023) Research and Application of Mental Health Prediction Method Based on Association Rule Mining [D]. Jinan University.
[2] Hao Wanlin. Research on Gray Correlation Algorithm of Factors for Mental Health Warning of College Students [J]. Electronic Design Engineering, 2022, 30(11): 12-16.
[3] Liu Jinming. Simplification and Application of SCL-90 Based on Machine Learning [D]. Qingdao University, 2020. 002011.
[4] Fan Wenrong. (2023) Research on Early Warning Model of College Students' Depression Tendency Based on Social Platform Data [D]. Nanjing University of Posts and Telecommunications.
[5] Zhu Lingyan. (2023) Data Analysis and Prediction of College Students' Mental Health Based on Data Mining [D]. Nanchang University.
[6] Gu Ronglong, Zhao Wenjie, Wang Lei. Application of data mining technology in the era of big data [J]. Science and Technology Innovation and Application, 2022, 12(5): 176-178.
[7] He Feng, Xing Lina, Ren Xuepu, et al. Investigation on mental health status of neurology practitioners in some areas of Hebei Province based on machine learning [J]. Journal of Brain and Neurological Diseases, 2024, 32(04): 240-246.
[8] Yang Juan. Research on the Application of Data Mining Technology in Predicting Mental Health Problems of Higher Vocational Students [J]. Science Consulting (Science and Technology · Management), 2021, (03): 161-162.
[9] Zhou Chengyi, Wang Yixin, Li Xiaoyuan. Intervention Model of College Students' Psychological Problems Based on Big Data [J]. Modern Communication, 2021, (17): 71-73.
[10] Hao Wanlin. Research on Gray Correlation Algorithm of Factors for Mental Health Warning of College Students [J]. Electronic Design Engineering, 2022, 30(11): 12-16.
[11] Tian Wei. Classification Algorithm Application of Big Data Mining — — Taking XGBoost as an Example [J]. Wireless Internet Technology, 2022, 19(19): 120-123.
[12] Mahendra A R P ,Irzam K R ,Sidharta S . Technique of Mental Health Issues Classification based on Machine Learning: Systematic Literature Review[J]. Procedia Computer Science, 2023, 227: 137-146.
[13] Wang Zhengli. (2023) Research and Application of Mental Health Prediction Method Based on Association Rule Mining [D]. Jinan University.
Copyright © 2025 Yuling Liu, Can Hou, Shaoyong Hong
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
