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

Table 2 The performance comparison of machine learning models on training data

No.	Description	Accuracy (%)	AUC	PPV/precision (%)	NPV (%)	Sensitivity/recall (%)	Specificity (%)	F1
1	Fine tree	71.6	0.64	42.9	79.8	37.8	83.0	0.40
2	Medium tree	74.4	0.70	48.9	79.1	30.1	89.4	0.37
3	Coarse tree	76.0	0.68	55.1	78.9	26.4	92.7	0.36
4	Linear discriminant	78.0	0.75	61.1	80.9	35.5	92.4	0.45
5	Logistic regression	78.1	0.75	62.2	80.6	33.9	93.0	0.44
6	Gaussian naïve Bayes	75.1	0.74	50.8	81.1	40.2	86.8	0.45
7	Kernel naïve Bayes	72.7	0.73	46.8	85.1	60.1	76.9	0.53
8	Linear SVM	77.0	0.74	64.4	78.1	19.9	96.3	0.30
9	Quadratic SVM	77.4	0.70	59.9	80.1	31.8	92.8	0.42
10	Cubic SVM	72.8	0.64	45.1	79.6	35.3	85.5	0.40
11	Fine Gaussian SVM	74.7	0.67		74.7		100.0
12	Medium Gaussian SVM	77.5	0.74	63.9	79.0	25.3	95.2	0.36
13	Coarse Gaussian SVM	75.7	0.74	66.2	76.0	7.9	98.6	0.14
14	Fine KNN	68.9	0.58	38.0	78.9	36.9	79.7	0.37
15	Medium KNN	76.5	0.71	59.7	78.1	21.0	95.2	0.31
16	Coarse KNN	76.6	0.75	78.1	76.5	10.0	99.1	0.18
17	Cosine KNN	76.6	0.72	57.9	79.2	27.6	93.2	0.37
18	Cubic KNN	77.0	0.72	62.0	78.5	22.6	95.3	0.33
19	Weighted KNN	76.5	0.71	56.7	79.4	28.8	92.6	0.38
20	Ensemble of boosted trees	76.9	0.74	57.3	80.3	33.6	91.6	0.42
21	Ensemble of bagged trees	77.2	0.74	58.9	80.2	32.5	92.3	0.42
22	Ensemble of subspace discriminant	78.3	0.76	66.7	79.7	28.3	95.2	0.40
23	Ensemble of subspace KNN	75.5	0.69	54.7	77.2	16.4	95.4	0.25
24	Ensemble of RUS boosted trees	70.4	0.76	44.2	86.3	66.4	71.7	0.53

AUC: Area under the curve; KNN: K-nearest neighbors; NPV: Negative predictive value; PPV: Positive predictive value; RUS: Random undersampling; SVM: Support vector machine.