Table 3.

Five-Fold Cross-Validation and Testing Results for Tumor Delivery Efficiency Using Different Machine Learning and Deep Learning Models

	DEmax		DE24		DE168		DETlast
Model	5-Fold CV	Test	5-Fold CV	Test	5-Fold CV	Test	5-Fold CV	Test
LR
R2	0.06 ± 0.05	0.08	0.10 ± 0.10	0.08	0.07 ± 0.03	0.06	0.07 ± 0.07	0.13
RMSE	3.98 ± 1.03	7.56	3.89 ± 0.61	6.56	2.18 ± 0.60	3.20	3.98 ± 0.88	4.73
MAE	2.42 ± 0.48	3.31	2.37 ± 0.24	2.70	1.29 ± 0.20	1.44	2.42 ± 0.44	2.46
KNN
R2	0.03 ± 0.04	0.06	0.04 ± 0.04	0.08	0.03 ± 0.04	0.04	0.01 ± 0.04	0.08
RMSE	4.05 ± 1.12	7.55	3.95 ± 0.71	6.51	2.31 ± 0.56	3.22	4.05 ± 1.01	4.77
MAE	2.36 ± 0.47	3.51	2.31 ± 0.30	2.82	1.33 ± 0.21	1.50	2.36 ± 0.43	2.59
RF
R2	0.19 ± 0.12	0.16	0.19 ± 0.16	0.17	0.19 ± 0.10	0.11	0.15 ± 0.16	0.29
RMSE	3.71 ± 1.03	7.15	3.64 ± 0.62	6.18	2.06 ± 0.61	3.17	3.72 ± 0.82	4.24
MAE	2.21 ± 0.48	2.92	2.17 ± 0.27	2.37	1.20 ± 0.21	1.30	2.22 ± 0.45	2.15
Bag
R2	0.09 ± 0.07	0.08	0.13 ± 0.12	0.08	0.10 ± 0.06	0.04	0.09 ± 0.09	0.15
RMSE	3.91 ± 1.06	7.49	3.86 ± 0.64	6.50	2.16 ± 0.58	3.22	3.91 ± 0.91	4.63
MAE	2.38 ± 0.47	3.34	2.34 ± 0.25	2.66	1.27 ± 0.19	1.35	2.38 ± 0.46	2.44
Gbm
R2	0.08 ± 0.08	0.09	0.12 ± 0.11	0.17	0.11 ± 0.06	0.05	0.08 ± 0.07	0.24
RMSE	3.91 ± 1.03	7.48	3.81 ± 0.62	6.30	2.16 ± 0.57	3.22	3.92 ± 0.85	4.46
MAE	2.42 ± 0.47	3.27	2.34 ± 0.26	2.60	1.30 ± 0.20	1.32	2.42 ± 0.42	2.38
R-SVM
R2	0.02 ± 0.03	0.23	0.04 ± 0.03	0.19	0.04 ± 0.03	0.14	0.02 ± 0.02	0.25
RMSE	4.12 ± 1.29	7.80	4.02 ± 0.87	6.76	2.28 ± 0.67	3.31	4.12 ± 1.12	4.97
MAE	1.93 ± 0.54	2.82	1.87 ± 0.35	2.32	1.06 ± 0.24	1.22	1.93 ± 0.47	2.08
LS-SVM
R2	0.02 ± 0.03	0.23	0.05 ± 0.03	0.18	0.05 ± 0.03	0.13	0.03 ± 0.03	0.24
RMSE	4.12 ± 1.29	7.81	4.02 ± 0.87	6.77	2.27 ± 0.66	3.31	4.12 ± 1.12	4.98
MAE	1.92 ± 0.54	2.83	1.86 ± 0.26	2.32	1.05 ± 0.24	1.22	1.93 ± 0.47	2.09
L2-SVM
R2	0.07 ± 0.06	0.14	0.11 ± 0.10	0.14	0.08 ± 0.04	0.18	0.08 ± 0.07	0.19
RMSE	4.01 ± 0.97	7.32	3.91 ± 0.59	6.37	2.23 ± 0.56	3.03	4.02 ± 0.78	4.54
MAE	2.52 ± 0.46	3.20	2.45 ± 0.26	2.61	1.38 ± 0.19	1.37	2.52 ± 0.42	2.39
DNN
R2	0.47 ± 0.20	0.70	0.40 ± 0.34	0.46	0.45 ± 0.24	0.33	0.35 ± 0.23	0.63
RMSE	3.58 ± 1.35	2.38	2.75 ± 0.92	3.10	1.96 ± 1.09	1.78	3.24 ± 1.04	3.01
MAE	2.20 ± 0.65	1.64	1.72 ± 0.50	1.84	1.10 ± 0.42	0.94	1.92 ± 0.54	1.81

Note: DE_max, DE₂₄, DE₁₆₈ and DE_Tlast represent the maximum tumor delivery efficiency (DE), DE at 24 h, 168 h, and the last sampling time, respectively.

Abbreviations: LR, linear regression; KNN, k-nearest neighbors; RF, random forest; Bag, bagged model; Gbm, stochastic gradient boosting; R-SVM, regular support vector machine; LS-SVM, least-squared support vector machine; L2-SVM, L2-regulated support vector machine; DNN, deep learning neural network; CV, cross-validation.