Publication Name	World Journal of Psychiatry
Manuscript ID	116013
Country	China

Category	Psychiatry
Manuscript Type	Case Control Study
Article Title	Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students
Manuscript Source	Unsolicited Manuscript
All Author List	Yan-Ge Wei, Lu-Han Yang, Shi-Sen Qin, Yuan-Le Chen, Jin-Nan Yan, Rong-Xun Liu, Yi-Meng Ma, Chao Wang, Zhen-Jie Song, Fei Wang and Guang-Jun Ji
Funding Agency and Grant Number	Funding Agency Grant Number 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 General Project of Henan Province Education Science No. 2023YB0135 Henan Provincial University Humanities and Social Science Research General Project No. 2025-ZZJH-317 Graduate Education Reform Project of Henan Province No. 2023SJGLX010Y
Corresponding Author	Guang-Jun Ji, Chief Physician, Head, Manager, Professor, Department of Early Intervention, The Second Affiliated Hospital of Xinxiang Medical University, Qianjin Road, Xinxiang 453002, Henan Province, China. jiguangjun@163.com
Key Words	Heart rate variability; Interpretable machine learning; Mental stress assessment; Chinese university students; Diastolic Pressure-Time Index; Systolic Pressure-Time Index
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 enhance early stress detection and personalized mental health monitoring in the Chinese university students.
Citation	Wei YG, Yang LH, Qin SS, Chen YL, Yan JN, Liu RX, Ma YM, Wang C, Song ZJ, Wang F, Ji GJ. Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students. World J Psychiatry 2026; In press

Received	2025-10-31 11:47
Peer-Review Started	2025-10-31 11:48
First Decision by Editorial Office Director	2025-12-05 07:13
Return for Revision	2025-12-05 07:13
Revised	2025-12-31 17:11
Publication Fee Transferred	2026-01-08 08:31
Second Decision by Editor	2026-02-09 02:47
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-02-09 06:25
Articles in Press	2026-02-09 06:25
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ISSN	2220-3206 (online)
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