Population-Level Predictive Variation in Machine Learning Diagnosis of Symptomatic Bacterial Vaginosis

Bacterial vaginosis (BV) is a prevalent vaginal syndrome, affecting millions of women globally. The complexity of the vaginal microbiome can challenge conventional diagnostic approaches, particularly for populations of women with healthy, yet diverse vaginal microbiomes. Advanced sequencing technologies coupled with machine learning (ML) offer promise in elucidating these complexities; however, ML models have been shown to be vulnerable to existing health disparities. To determine the ability of ML models to perform equitably, this study evaluates the performance of ML algorithms in predicting symptomatic BV across different ethnic groups using 16S rRNA sequencing data. Results indicate differential predictive performance across ethnicities, with models exhibiting lower accuracy for Black women. Moreover, we found variation in significant bacterial taxa for predicting BV by ethnicity. Future research aims to explore these factors and validate findings in larger, more diverse cohorts, with the goal of improving BV diagnosis and mitigating health disparities across ethnic groups.