Bacterial vaginosis (BV) is a disease associated with the vagina microbiome. It is highly prevalent and is characterized by symptoms including odor, discharge and irritation. No single microbe has been found to cause BV. In this paper we use random forests and logistic regression classifiers to model the relationship between the microbial community and BV. We use subsets of the microbial community features in order to determine which features are important to the classification models.
We find that models generated using logistic regression and random forests perform nearly identically and identify largely similar important features. Only a few features are necessary to obtain high BV classification accuracy. Additionally, there appears to be substantial redundancy between the microbial community features.
These results are in contrast to a previous study in which the important features identified by the classifiers were dissimilar. This difference appears to be the result of using different feature importance measures. It is not clear whether machine learning classifiers are capturing patterns different from simple correlations.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 August 2015.
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