Table 3:

Classification performance of mpMRI models using various classifiers along with recursive feature elimination. Best AUC (Mean AUC) was reported for each classifier as the classification performance metric.

Classifier	RCB	RFS	DSS
XGBoost	0.9430 (0.8577)	0.8333 (0.7672)	0.9200 (0.9052)
AdaBoost	0.8523 (0.8112)	0.7666 (0.7009)	0.9200 (0.8342)
Linear SVM	0.8767 (0.8450)	0.8666 (0.7777)	0.9200 (0.7797)
LDA	0.7544 (0.6608)	0.8833 (0.7620)	0.9705 (0.9019)
LR	0.8684 (0.8207)	0.8666 (0.8259)	0.9200 (0.8130)
SGD	0.8303 (0.7086)	0.8416 (0.7787)	0.9000 (0.7605)
Decision Tree	0.8113 (0.7729)	0.7916 (0.5654)	0.8666 (0.8028)
RF	0.8857 (0.8364)	0.7916 (0.6682)	0.8666 (0.8559)

Note: RCB, residual class burden; recurrence-free survival; DSS, disease-specific survival, RFS, recurrence-free survival, SVM, linear support vector machine; LDA, linear discriminant analysis; LR, logistic regression; RF, random forests; SGD, stochastic gradient descent; decision tree