Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

Table 3 Comparison of machine learning model performance

	Models	Training cohort			Validation cohort
		Accuracy	Precision	AUC	Accuracy	Precision	AUC
Cli	SVM	0.720	0.750	0.736	0.688	0.867	0.686
	RF	0.720	0.750	0.768	0.688	0.867	0.680
	KNN	0.693	0.667	0.763	0.672	0.756	0.701
	XGB	0.720	0.750	0.768	0.688	0.867	0.680
	LightGBM	0.720	0.750	0.761	0.688	0.867	0.693
Rad	SVM	0.673	0.695	0.726	0.719	0.853	0.756
	RF	0.753	0.761	0.849	0.672	0.744	0.688
	KNN	0.713	0.716	0.809	0.656	0.778	0.690
	XGBoost	0.860	0.849	0.945	0.594	0.700	0.588
	LightGBM	0.727	0.696	0.820	0.688	0.729	0.629
Con	SVM	0.727	0.754	0.778	0.688	0.867	0.739
	RF	0.727	0.731	0.820	0.797	0.872	0.777
	KNN	0.767	0.747	0.844	0.719	0.816	0.790
	XGB	0.840	0.875	0.924	0.609	0.743	0.656
	LightGBM	0.807	0.787	0.892	0.719	0.800	0.771

Five model performance evaluation results from clinical-radiologic, radiomics andcombined models. Cli: Clinical-radiologic model; Rad: Radiomics model; Con: Combined model.