Table 2.

Comparison of performance among various ML methods used for construction of the binary MOLT model

Cohort	Model	Index
		AUROC (95% CI)	ACC	AUPR	F1 score	Sensitivity	Specificity
LG cohort (training)	Random forest	1.000 (1.000–1.000)	0.996	1.000	0.997	0.987	1.000
	LightGBM	0.999 (0.999–1.000)	0.983	0.999	0.988	0.956	0.996
	XGBoost	0.891 (0.878–0.904)	0.836	0.842	0.888	0.573	0.961
	Decision tree	0.836 (0.820–0.853)	0.825	0.744	0.874	0.681	0.894
	SVM	0.920 (0.907–0.932)	0.872	0.892	0.911	0.680	0.963
	Logistic regression	0.811 (0.793–0.829)	0.787	0.707	0.855	0.501	0.923
	KNN	0.992 (0.990–0.995)	0.907	0.982	0.935	0.731	0.990
LG cohort (internal validation)	Random forest	0.875 (0.855–0.896)	0.824	0.816	0.878	0.566	0.955
	LightGBM	0.907 (0.891–0.923)	0.842	0.851	0.887	0.650	0.938
	XGBoost	0.852 (0.832–0.873)	0.798	0.774	0.862	0.504	0.947
	Decision tree	0.826 (0.803–0.849)	0.791	0.748	0.848	0.613	0.881
	SVM	0.817 (0.793–0.842)	0.772	0.719	0.841	0.500	0.910
	Logistic regression	0.781 (0.754–0.809)	0.757	0.672	0.835	0.431	0.923
	KNN	0.713 (0.684–0.743)	0.705	0.582	0.799	0.354	0.883
BS cohort (external validation)	Random forest	0.862 (0.819–0.904)	0.828	0.865	0.864	0.676	0.936
	LightGBM	0.822 (0.774–0.870)	0.802	0.827	0.841	0.669	0.897
	XGBoost	0.725 (0.667–0.782)	0.699	0.709	0.768	0.483	0.853
	Decision tree	0.678 (0.615–0.741)	0.731	0.689	0.783	0.586	0.833
	SVM	0.856 (0.812–0.900)	0.808	0.844	0.828	0.834	0.789
	Logistic regression	0.800 (0.754–0.846)	0.722	0.734	0.771	0.614	0.799
	KNN	0.816 (0.770–0.862)	0.751	0.781	0.806	0.559	0.887

Cells set in bold/italic represent the first/second best performance in each cohort.

ACC, accuracy; AUPR, area under the precision-recall curve; CI, confidence interval; KNN, k-nearest neighbors; ML, machine learning; MOLT, machine learning of obstructive jaundice based on common laboratory tests; SVM, support vector machine.