Figure 1
A linear boundary SVM and logistic regression outperform random forests in identifying subjects with MDD. Three supervised machine-learning methods were applied to discriminate MDD subjects from control subjects: (left) logistic regression, (center) random forests, and (right) support vector machines. To improve model prediction and identify an optimal transcript set, backward selection was performed. Backward selection removes transcripts from the explanatory variables in the classification model individually; for each iteration, we recalculate model accuracy, sensitivity, and specificity. The transcript associated with the lowest accuracy is permanently removed from the set of predictive variables and the process is repeated. Random forests had less accuracy than logistic regression or SVMs, suggesting that nonlinear contributions of the explanatory variables did not provide additional accuracy to the model. Logistic regression and SVMs with a linear boundary both had high accuracy, 92.2% and 90.6%, respectively. MDD, major depressive disorder; SVM, support vector machine.
