Table 3.

The comparison of different classifiers

Data set	Classifier	Acc(%)	AUC	Fscore(%)	MCC(%)	SP(%)	SE(%)
HCCs	RF	90.48	0.9438	79.89	73.86	95.43	75.39
	GBDT	91.69	0.9538	83.11	77.58	95.11	81.31
	XGBoost	90.74	0.9554	82.89	76.73	91.23	88.94
	LightGBM	92.06	0.9616	84.66	78.97	93.73	86.84
	FCNN	90.27	0.9402	80.16	73.77	94.24	78.14
HepG2	RF	82.46	0.9027	78.98	63.92	84.94	78.93
	GBDT	81.80	0.8990	78.34	62.63	83.92	78.80
	XGBoost	79.42	0.9131	79.09	62.39	93.14	69.53
	LightGBM	83.20	0.9213	81.73	67.36	89.96	78.32
	FCNN	80.97	0.8841	76.76	60.70	84.93	75.35

¹RF [28] is an ensemble learning model that uses the idea of bagging and the random selection of features to avoid data over-fitting

²GBDT [60] is a non-parallel model that uses the gradient from previous tree as the input for the next tree

³XGBoost [53] is an improved GBDT algorithm. The reference indicator of XGBoost is completely redefined when the tree leaf nodes split

⁴LightGBM [36] is based on the GBDT algorithm and employs sample selection and feature mergence to reduce the running time

⁵FCNN represents the Fully Connected Neural Network

⁶The boldface is the best value in the column