Fig. 4: Computational model predicts pregnancy outcome using vaginal microbiome and inflammation data.
From: Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success
A Confusion matrix for the three models—two models at time point 2 (egg retrieval/2nd ultrasound) using either bacterial features alone or combined with inflammatory features. The third model is at time point 3 (embryo transfer) using only inflammatory features. F1 scores for each model are shown on the top. B Global feature importance ranking graph for each model. Features are ordered by importance in descending order from top to bottom. Each observation is represented by a dot, with the initial feature value indicated by the dot’s color according to the color map (left). The x-axis represents the influence of each feature, with a vertical line marking the baseline. Points to the right of the baseline indicate positive influence on pregnancy outcome, while points to the left indicate negative influence on pregnancy outcome. Variables are ordered by global importance, with the most important feature listed first and the least important listed last. For an importance ranking presentation, the absolute influence distribution SHAP graph for each model is shown (right). The feature importance values were normalized to the highest-ranked feature importance value. Purple and orange bars represent bacteria and cytokines features importances, respectively.
