Table 3.

Predictive Accuracy of the ML Models and Comparisons with Conventional Logistic Regression for the 2-Year PFS and OS.

OS 2-years
Model	Accuracy	AUC_P	AUC_N	Precision	Recall	F-score	G-score
SVM – Quadratic Kernel	72.9%	.66	.418	.7182	.9076	.8018	.8074
SVM – Cubic Kernel	68.2%	.58	.41	.7252	.8719	.7917	.7951
Logistic Regression	66.5%	.59	.413	.7209	.9169	.8071	.8130
Gaussian Naïve Bayes	66.0%	.63	.463	.6934	.9879	.8148	.8276
KNN – 5 neighbors	71.8%	.63	.443	.7009	.8656	.7742	.7787
KNN – 10 neighbors	69.4%	.62	.433	.7081	.8350	.7661	.7688
Ensemble – Bagged Trees	68.8%	.60	.432	.7086	.8425	.7695	.7725
Ensemble – Subspace Discriminant	71.8%	.61	.411	.7154	.9270	.8071	.8141
PFS 2-years
Model	Accuracy		AUC	Precision	Recall	F-score	G-score
SVM – Quadratic Kernel	65.50%	.62	.469	.5160	.8893	.6530	.6774
SVM – Cubic Kernel	58.20%	.52	.485	.4309	.7286	.5415	.5603
Logistic Regression	56.50%	.58	.468	.5049	.8478	.6384	.6619
Gaussian Naïve Bayes	58.80%	.55	.49	.4356	.8373	.5731	.6039
KNN – 5 neighbors	57.60%	.54	.452	.4574	.5834	.5127	.5165
KNN – 10 neighbors	56.18%	.58	.446	.4643	.5947	.5214	.5254
Ensemble – Bagged Trees	55.30%	.52	.494	.4180	.7497	.5367	.5598
Ensemble – Subspace Discriminant	59.40%	.58	.475	.5112	.9096	.6546	.6819