Figure 2

Schematic diagram of the machine learning workflow. The dataset was split into training (80% of the whole dataset) and holdout (20% of dataset) subsets. Feature selection and classification hyperparameters were optimized by generating machine learning models using support vector machines (SVM) for all predefined combinations of the hyperparameters N and C. The model performance associated with each combination was assessed using a fivefold cross-validation technique based on ROC analyses comparing model predictions with the assigned pathology labels. The final model was trained on the complete training set using the hyperparameters that yielded the lowest number of false positives and false negatives.