Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China

doi:10.5498/wjp.v15.i8.106622

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 15, Issue 8

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Supplementary Materials of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (195)

All Articles published online

The chart showing PDF series, HTML series, Figures (1-9) series, Tables (1-2) series.

Item

Count

PDF

HTML

Figures (1-9)

Tables (1-2)

Sum=129

Publishing Process of This Article

The chart showing Browse series, Download series.

Item

Count

Browse

Download

Sum=63

Aug 19, 2025 (publication date) through Aug 11, 2025

Times Cited of This Article

Times Cited (0)

Journal Information of This Article

Publication Name

World Journal of Psychiatry

ISSN

2220-3206

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Observational Study

World J Psychiatry. Aug 19, 2025; 15(8): 106622
Published online Aug 19, 2025. doi: 10.5498/wjp.v15.i8.106622

Full Size Figure Download Figure

Figure 6 Nomogram for predicting the risk of depression based on insomnia, anxiety symptoms, chronic disease, exercise, and age. The total points are used to calculate the linear predictor and risk probability.

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; 15(8): 106622
URL: https://www.wjgnet.com/2220-3206/full/v15/i8/106622.htm
DOI: https://dx.doi.org/10.5498/wjp.v15.i8.106622