Quality of life identification by unsupervised cluster analysis: A new approach to modelling the burden of endometriosis

Alexandre Vallée, Maxence Arutkin, Pierre-François Ceccaldi and Jean-Marc Ayoubi

PLOS ONE, 2025, vol. 20, issue 1, 1-11

Abstract: Background: Symptoms frequently associated with endometriosis affect quality of life (QoL). Our aim investigated the hypothesis that cluster analysis can be used to identify homogeneous phenotyping subgroups of women according to the burden of the endometriosis for their QoL, and then to investigate the phenotype differences observed between these subgroups. Methods: We developed an anonymous online survey, which received responses from 1,586 French women with endometriosis. K‐means, a major clustering algorithm, was performed to show structure in data and divide women into groups based on the burden of endometriosis. This was defined using 9 dimensions. Multivariable logistic regression was performed to highlight the association between QoL and several factors. Covariables were age, BMI, smoking, education, children, marital status and surgery. Results: K‐means clustering was implemented with 8 clusters (optimal CCC value of 17.2162). In one cluster, women presented a high level of QoL and represented 234 women for 60% of women with a high level of QoL, and another with 410 women for 34% of women with worse QoL. Independent factors determining high QoL were age (over 45 years compared to below 25 years, OR = 0.17 [0.07–0.46], p

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0317178 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 17178&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0317178

DOI: 10.1371/journal.pone.0317178

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().