Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis.
Arch Gynecol Obstet
; 309(3): 1053-1063, 2024 03.
Article
en En
| MEDLINE
| ID: mdl-38310145
ABSTRACT
INTRODUCTION:
This study used an unsupervised machine learning algorithm, sidClustering and random forests, to identify clusters of risk behaviors of Bacterial Vaginosis (BV), the most common cause of abnormal vaginal discharge linked to STI and HIV acquisition.METHODS:
Participants were 391 cisgender women in Miami, Florida, with a mean of 30.8 (SD = 7.81) years of age; 41.7% identified as Hispanic; 41.7% as Black and 44.8% as White. Participants completed measures of demographics, risk behaviors [sexual, medical, and reproductive history, substance use, and intravaginal practices (IVP)], and underwent collection of vaginal samples; 135 behavioral variables were analyzed. BV was diagnosed using Nugent criteria.RESULTS:
We identified four clusters, and variables were ranked by importance in distinguishing clusters Cluster 1 nulliparous women who engaged in IVPs to clean themselves and please sexual partners, and used substances frequently [n = 118 (30.2%)]; Cluster 2 primiparous women who engaged in IVPs using vaginal douches to clean themselves (n = 112 (28.6%)]; Cluster 3 primiparous women who did not use IVPs or substances [n = 87 (22.3%)]; and Cluster 4 nulliparous women who did not use IVPs but used substances [n = 74 (18.9%)]. Clusters were related to BV (p < 0.001). Cluster 2, the cluster of women who used vaginal douches as IVPs, had the highest prevalence of BV (52.7%).CONCLUSIONS:
Machine learning methods may be particularly useful in identifying specific clusters of high-risk behaviors, in developing interventions intended to reduce BV and IVP, and ultimately in reducing the risk of HIV infection among women.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Infecciones por VIH
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Vaginosis Bacteriana
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
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Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Arch Gynecol Obstet
Asunto de la revista:
GINECOLOGIA
/
OBSTETRICIA
Año:
2024
Tipo del documento:
Article