Construction and evaluation of a liver cancer risk prediction model based on machine learning

Table 3 Performance of modeling indicators and prediction models in the diagnosis of hepatocellular carcinoma

Variables	AUC (95%CI)	Cut-off	Accuracy (%)	Se (%)	Sp (%)	PPV (%)	NPV (%)	F1 Score
Age	0.638 (0.582, 0.693)	53.50	62.57	59.90	65.26	63.54	61.69	0.62
WBC (109/L)	0.703 (0.651, 0.755)	4.10	67.54	65.63	69.47	68.48	66.67	0.67
RBC (1012/L)	0.653 (0.598, 0.708)	4.22	63.09	70.83	55.26	61.54	65.22	0.66
PLT (109/L)	0.735 (0.685, 0.785)	109.00	68.32	55.73	81.05	74.83	64.44	0.64
AFP (ng/mL)	0.776 (0.729, 0.823)	19.05	73.30	59.90	86.84	82.14	68.18	0.69
PIVKA-II (mAU/mL)	0.842 (0.802, 0.882)	90.91	79.32	69.79	88.95	86.45	74.45	0.77
LR-training set	0.850 (0.812, 0.888)	0.47	78.01	63.02	93.16	90.30	71.37	0.74
LR-validation set	0.827 (0.764, 0.890)	0.47	74.55	65.06	84.15	80.60	70.41	0.79
SVM-training set	0.860 (0.822, 0.898)	0.54	80.10	71.35	88.95	86.71	75.45	0.72
SVM-validation set	0.803 (0.735, 0.871)	0.54	72.73	68.67	76.83	75.00	70.79	0.77
RF-training set	0.969 (0.955, 0.984)	0.52	92.15	88.02	96.32	96.02	88.83	0.78
RF-validation set	0.858 (0.800, 0.917)	0.52	80.00	75.90	84.15	82.89	77.53	0.72
LASSO-training set	0.845 (0.806, 0.884)	0.18	78.80	69.27	88.42	85.81	74.01	0.72
LASSO-validation set	0.831 (0.769, 0.893)	0.18	72.73	68.67	76.83	75.00	70.79	0.81
ASAP-training set	0.866 (0.829, 0.903)	0.93	82.20	73.96	90.53	88.75	77.48	0.92
ASAP-validation set	0.813 (0.747, 0.879)	0.93	73.94	61.45	86.59	82.26	68.93	0.70

Se: Sensitivity; Sp: Specificity; PPV: Positive predictive value: NPV: Negative predictive value; AUC: Area under the curve; LASSO: Least absolute shrinkage and selection operator; LR: Logistic regression; SVM: Support vector machine; RF: Random forest; LASSO: Least absolute shrinkage and selection operator; AUC: Area under the curve; WBC: White blood cell; RBC: Red blood cell; PLT: Platelet; AFP: Alpha-fetoprotein; PIVKA-II: Protein induced by vitamin K absence or antagonist II.