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World J Psychiatry. Jun 19, 2026; 16(6): 116013
Published online Jun 19, 2026. doi: 10.5498/wjp.v16.i6.116013
Published online Jun 19, 2026. doi: 10.5498/wjp.v16.i6.116013
Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students
Yan-Ge Wei, Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, Henan Province, China
Lu-Han Yang, Shi-Sen Qin, Yuan-Le Chen, Jin-Nan Yan, Yi-Meng Ma, Chao Wang, Zhen-Jie Song, Guang-Jun Ji, Department of Early Intervention, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, Henan Province, China
Rong-Xun Liu, School of Public Health, Xinxiang Medical University, Xinxiang 453002, Henan Province, China
Fei Wang, Department of Psychiatry, Yale University, School of Medicine, New Haven, CT 06511, United States
Co-corresponding authors: Fei Wang and Guang-Jun Ji.
Author contributions: Wei YG and Yang LH conceptualized and designed the study; Qin SS, Chen YL, Yan JN, and Liu RX collected and managed the data; Ma YM, Wang C, and Song ZJ conducted the statistical analysis; Wei YG and Yang LH wrote the original draft; Ji GJ and Wang F reviewed and edited the manuscript, and they contributed equally to this manuscript and are co-corresponding authors. All authors have read and approved the final manuscript.
Supported by the Young and Middle-aged Health Science and Technology Innovation Talents Project of Henan Province, No. JQRC2025014; Graduate Education Reform Project of Henan Province, No. 2023SJGLX063Y and No. 2023SJGLX010Y; General Project of Henan Province Education Science, No. 2023YB0135; and Henan Provincial University Humanities and Social Science Research General Project, No. 2025-ZZJH-317.
Institutional review board statement: The study was approved by the Research Ethics Committee of the Second Affiliated Hospital of Xinxiang Medical University (Approval Code: XYEFYLL-2023-35-4). The study was conducted according to the Declaration of Helsinki. Written informed consent was obtained from all participants and their legal guardians.
Informed consent statement: Written informed consent was obtained from all individual participants included in the study. All data were anonymized to protect participant privacy.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement:
The data that support the findings of this study are available on request from the corresponding author (jiguangjun@163.com) . The data are not publicly available due to privacy or ethical restrictions.
Corresponding author: Guang-Jun Ji, PhD, Chief Physician, Head, Professor, Department of Early Intervention, The Second Affiliated Hospital of Xinxiang Medical University, Qianjin Road, Xinxiang 453002, Henan Province, China. jiguangjun@163.com
Received: October 31, 2025
Revised: December 31, 2025
Accepted: February 9, 2026
Published online: June 19, 2026
Processing time: 209 Days and 16.5 Hours
Revised: December 31, 2025
Accepted: February 9, 2026
Published online: June 19, 2026
Processing time: 209 Days and 16.5 Hours
Core Tip
Core Tip: This study integrated heart rate variability (HRV) parameters with six machine learning algorithms to distinguish between individuals with and without mental stress among Chinese university students. The random forest classifier exhibited the optimal classification performance. Among eleven significantly altered HRV parameters in the stress group, the SHapley Additive exPlanations analysis identified the Diastolic/Systolic Pressure-Time Index of the heart as the most significant parameter. Combining HRV parameters and a random forest model provides an objective methodology to en