Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity
Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity
About this item
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Author / Creator
Zhao, Hongguo , Liu, Peng , Chen, Fei , Wang, Mengjuan , Liu, Jiaxi , Fu, Xiling , Yu, Hang , Nai, Manman , Li, Lei and Li, Xinbin
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
This study develops predictive models for Chinese female patients with VL utilizing machine learning techniques. The aim is to create an effective model that can assist in clinical diagnosis and treatment of vaginal relaxation, thereby enhancing women’s pelvic floor health. In total, 1184 women with VL have been randomly selected and categorized in...
Alternative Titles
Full title
Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_32b6fd5b310f41f3853502156b9112eb
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_32b6fd5b310f41f3853502156b9112eb
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-86931-x
