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

Table 3 The performance of machine learning models and other non-alcoholic fatty liver disease indices on testing data

No.	Description	Accuracy (%)	AUC	PPV/precision (%)	NPV (%)	Sensitivity/recall (%)	Specificity (%)	F1
Machine learning models
1	Ensemble of subspace discriminant	77.7	0.78	66.7	78.8	23.7	96	0.35
2	Coarse trees	74.9	0.72	50.8	78.3	24.5	92	0.33
3	Ensemble of RUS boosted trees	71.1	0.79	45.5	88.4	72.7	70.6	0.56
NAFLD indices
4	Fatty liver index	68.6	0.74	42.4	86.6	68.6	68.6	0.52
5	Hepatic steatosis index	65.1	0.70	37.9	83.3	60.4	66.6	0.47
6	Triglyceride and glucose index	56.9	0.69	34.8	88.3	80.8	48.8	0.49

AUC: Area under the curve; NAFLD: Non-alcoholic fatty liver disease; NPV: Negative predictive value; PPV: Positive predictive value; RUS: Random undersampling.