Figure 4.

Averaged classification performance of four classifiers and box plots for the selected features that were used for training a random forest model. (A) Four different classifiers were used based on the data from the training set with the highest average classification performance (i.e. accuracy) achieved with random forest (balanced accuracy of ~59%). (B) Box plots of the six most important features that were used for training a RandomForest model based on the training set and were the most differential between patients with endometriosis and controls. Red color designate patients with endometriosis and blue color controls. Machine Learning models used: glmnet, elastic-net regularized generalized models; kknn, Weighted k-Nearest Neighbors; rpart, Recursive Partitioning and Regression Trees; rf, RandomForest. Dashed red line indicates expected balanced accuracy of a random chance.