Predicting chemotherapy-induced myelosuppression in colorectal cancer: An interpretable, machine learning-based nomogram

Table 2 Performance of 10 machine learnings for predicting myelosuppression after first-line chemotherapy for colorectal cancer

MLs	AUC (95%CI)	AUPRC (95%CI)	Accuracy (%)	Sensitivity (%)	Specificity (%)	F1 score	PPV	NPV
Training set
Adaptive boosting	0.88 (0.85-0.91)	0.79 (0.73-0.84)	0.79	0.49	0.94	0.61	0.81	0.78
Artificial neural network	0.96 (0.95-0.97)	0.94 (0.91-0.96)	0.73	0.53	0.83	0.57	0.61	0.78
Decision tree	1.00 (1.00-1.00)	1.00 (1.00-1.00)	1.00	1.00	1.00	1.00	1.00	1.00
Extra trees	1.00 (1.00-1.00)	1.00 (1.00-1.00)	1.00	1.00	1.00	1.00	1.00	1.00
Gradient boosting machine	0.99 (0.99-1.00)	0.99 (0.99-1.00)	0.98	0.93	0.99	0.96	0.99	0.97
K-Nearest neighbors	0.91 (0.89-0.93)	0.79 (0.75-0.84)	0.90	0.85	0.92	0.85	0.85	0.92
Logistic regression	0.75 (0.71-0.79)	0.57 (0.50-0.65)	0.69	0.25	0.92	0.35	0.61	0.70
Random forest	1.00 (0.99-1.00)	1.00 (0.99-1.00)	1.00	1.00	1.00	1.00	1.00	1.00
Support vector machine	0.87 (0.83-0.90)	0.79 (0.73-0.84)	0.68	0.05	1.00	0.10	1.00	0.67
Extreme gradient boosting	1.00 (0.99-1.00)	1.00 (0.99-1.00)	1.00	1.00	1.00	1.00	1.00	1.00
Validation set
Adaptive boosting	0.83 (0.76-0.89)	0.72 (0.60-0.85)	0.78	0.35	0.95	0.47	0.71	0.79
Artificial neural network	0.69 (0.60-0.78)	0.61 (0.48-0.74)	0.65	0.42	0.75	0.40	0.39	0.77
Decision tree	0.70 (0.62-0.78)	0.52 (0.41-0.64)	0.82	0.81	0.83	0.72	0.65	0.92
Extra trees	0.94 (0.89-0.97)	0.90 (0.82-0.96)	0.87	0.72	0.93	0.76	0.79	0.89
Gradient boosting machine	0.92 (0.86-0.97)	0.90 (0.84-0.95)	0.83	0.67	0.89	0.69	0.71	0.88
K-Nearest neighbors	0.75 (0.67-0.83)	0.62 (0.48-0.74)	0.80	0.70	0.85	0.67	0.64	0.88
Logistic regression	0.67 (0.59-0.76)	0.49 (0.38-0.65)	0.68	0.20	0.86	0.27	0.38	0.74
Random forest	0.96 (0.93-0.98)	0.93 (0.87-0.97)	0.88	0.72	0.95	0.78	0.84	0.90
Support vector machine	0.75 (0.67-0.83)	0.65 (0.52-0.77)	0.75	0.12	1.00	0.21	1.00	0.74
Extreme gradient boosting	0.97 (0.94-0.99)	0.92 (0.83-0.99)	0.88	0.79	0.92	0.79	0.79	0.92

AUC: Area under the curve; ML: Machine learning; AUPRC: Area under the precision-recall curve; CI: Confidence interval; PPV: Positive predictive value; NPV: Negative predictive value.