Publication Name	World Journal of Psychiatry
Manuscript ID	106622
Country	China

Category	Psychiatry
Manuscript Type	Observational Study
Article Title	Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China
Manuscript Source	Unsolicited Manuscript
All Author List	Jia-Min Chen, Mei Rao, Yu-Ting Wei, Qiong-Gui Zhou, Jun-Long Tao, Shi-Bin Wang and Bo Bi
Funding Agency and Grant Number	Funding Agency Grant Number Longyan City Science and Technology Plan Project 2024 LYF17067
Corresponding Author	Bo Bi, School of Public Health, Hainan Medical University, Xueyuan Road, Longhua District, Haikou 571199, Hainan Province, China. bibo@hainmc.edu.cn
Key Words	Depressive symptoms; Women’s mental health; Machine learning; Predictive modeling; Shapley additive explanations; Nomogram; Guangdong province
Core Tip	This study leverages machine learning to develop a highly accurate predictive model for depressive symptoms in women. Ablation studies systematically validated the critical contributions of these top-ranked Shapley Additive exPlanations features, demonstrating significant performance degradation upon their removal. The light gradient boosting machine model achieved superior performance, supported by Shapley Additive exPlanations for interpretability and a nomogram for clinical application. This innovative approach offers a practical tool for early detection and personalized intervention, addressing the limitations of traditional methods. Findings highlight the potential of machine learning to enhance women’s mental health outcomes, with implications for improving diagnostic precision and treatment strategies in diverse clinical settings.
Citation	Chen JM, Rao M, Wei YT, Zhou QG, Tao JL, Wang SB, Bi B. Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China. World J Psychiatry 2025; In press

Received	2025-03-04 07:58
Peer-Review Started	2025-03-04 07:58
To Make the First Decision
Return for Revision	2025-04-03 08:43
Revised	2025-04-17 08:30
Second Decision	2025-06-25 02:43
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-06-25 08:49
Articles in Press	2025-06-25 08:49
Publication Fee Transferred	2025-04-18 01:50
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2220-3206 (online)
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