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Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis

  • General Gynecology
  • Published:
Archives of Gynecology and Obstetrics Aims and scope Submit manuscript

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.

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Data availability

Data is available from the corresponding author, Maria Luisa Alcaide.

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Funding

This work was supported by National Institutes of Health grants to the University of Miami [R01AI138718 to M.L.A], Center for AIDS Research grant [P30A1073961 to M.L.A.] and the Center for HIV and Research in Mental Health [P30MH116867 to D.L.J.] This work was also partially funded by a Ford Foundation Fellowship to VJR, administered by the National Academies of Sciences, Engineering, and Medicine, a PEO Scholar Award from the PEO Sisterhood, and NIMH R36MH127838.

Author information

Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, USA

    Violeta J. Rodriguez & Deborah L. Jones

  2. Department of Psychology, University of Georgia, Athens, USA

    Violeta J. Rodriguez

  3. Division of Biostatistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, USA

    Yue Pan

  4. Division of Infectious Diseases, Department of Medicine, University of Miami Miller School of Medicine, 1951 NW 10th Avenue, Suite 2300, Miami, FL, 33136, USA

    Ana S. Salazar, Nicholas Fonseca Nogueira, Patricia Raccamarich & Maria L. Alcaide

  5. Surgical Outcomes and Precision Medicine Research Division, Department of Surgery, University of Minnesota, Minneapolis, USA

    Nichole R. Klatt

Authors
  1. Violeta J. Rodriguez
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  2. Yue Pan
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  8. Maria L. Alcaide
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Contributions

VJR protocol/project development, manuscript writing, data analysis. YP data analysis. AS data collection or management, manuscript editing. NFN data collection or management, manuscript editing. PR data collection or management, manuscript editing. NRK protocol/project development. DLJ protocol/project development. MLA protocol/project development, manuscript editing.

Corresponding author

Correspondence to Maria L. Alcaide.

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Rodriguez, V.J., Pan, Y., Salazar, A.S. et al. Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis. Arch Gynecol Obstet 309, 1053–1063 (2024). https://doi.org/10.1007/s00404-023-07360-7

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  • DOI: https://doi.org/10.1007/s00404-023-07360-7

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