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

Table 4 Performance comparison of extreme gradient boosting, clinic nomogram, and clinic-machine learning nomograms

Model	AUC (95%CI)	AUPRC (95%CI)	Accuracy (%)	Sensitivity (%)	Specificity (%)	F1 score	PPV	NPV
Training set
Extreme gradient boosting	1.00 (1.00-1.00)	1.00 (0.99-1.00)	1.00	1.00	1.00	1.00	1.00	1.00
Clinic nomogram	0.68 (0.63-0.72)	0.49 (0.43-0.56)	0.69	0.25	0.92	0.69	0.61	0.71
Clinic-machine learning nomogram	0.99 (0.99-1.00)	0.99 (0.99-1.00)	1.00	1.00	1.00	1.00	1.00	1.00
Validation set
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
Clinic nomogram	0.61 (0.52-0.71)	0.48 (0.36-0.62)	0.68	0.21	0.86	0.27	0.38	0.74
Clinic-machine learning nomogram	0.96 (0.92-0.98)	0.93 (0.87-0.98)	0.90	0.77	0.95	0.80	0.85	0.91

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