GA-Based Selection of Vaginal Microbiome Features Associated with Bacterial Vaginosis.

Abstract

In this paper, we successfully apply GEFeS (Genetic & Evolutionary Feature Selection) to identify the key features in the human vaginal microbiome and in patient meta-data that are associated with bacterial vaginosis (BV). The vaginal microbiome is the community of bacteria found in a patient, and meta-data include behavioral practices and demographic information. Bacterial vaginosis is a disease that afflicts nearly one third of all women, but the current diagnostics are crude at best. We describe two types of classifies for BV diagnosis, and show that each is associated with one of two treatments. Our results show that the classifiers associated with the 'Treat Any Symptom' version have better performances that the classifier associated with the 'Treat Based on N-Score Value'. Our long term objective is to develop a more accurate and objective diagnosis and treatment of BV.