Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database

Table 4 The performance comparison of published machine learning models on non-alcoholic fatty liver disease prediction

Ref.	Type of data/country or territory of data	Number of train/ external testing data	Model	Accuracy (%)	AUC	Sensitivity (%)	Specificity (%)	F1
Sorino et al[33], 2020	Population/Italy	2920/50	Support vector machine	681	N/A	98.5	100	N/A
Wu et al[13], 2019	Hospital/Taiwan	577/NA	Random forest	86.51	0.9251	87.21	85.91	N/A
Islam et al[36], 2018	Hospital/Taiwan	994/NA	Logistic regression	701	0.7631	74.11	64.91	N/A
Ma et al[12], 2018	Hospital/China	10508/NA	Bayesian network	82.921	N/A	67.51	87.81	0.6551
Perveen et al[14], 2018	Primary care network/Canada	64%/34% of 40637	Decision trees	N/A	0.73	73	N/A	0.67
Yip et al[15], 2017	Hospital/Hong Kong	500/442	Ridge regression	87	0.87	92	90	N/A
Birjandi et al[37], 2016	Hospital/Iran	359/1241	Decision trees	75	0.75	73	77	N/A
Our study	Population based/United States	2265/970	Ensemble of RUS boosted trees	71.1	0.79	72.7	70.6	0.56
			Coarse trees	74.9%	0.72	24.5%	92%	0.33

¹Cross-validation performance (no separate dataset designated for testing the performance).

RUS: Random undersampling; AUC: area under the receiver operating characteristic curve; N/A: Not applicable; NA: Not available.