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

Table 1 Baseline characteristics of participants in training and testing data

	Training data (n = 2265)	Testing data (n = 970)	P value
Demographic
Age (yr)	43 (29)	43.5 (28)	0.328
Gender (male) (%)	944 (41.68)	428 (44.12)	0.197
Race/ethnicity
White (non-Hispanic) (%)	959 (42.34)	392 (40.41)	0.308
Black (non-Hispanic) (%)	627 (27.68)	271 (27.94)	0.882
Mexican American (%)	576 (25.43)	254 (26.19)	0.652
Others (%)	103 (4.55)	53 (5.46)	0.265
Body measurement
Body mass index (kg/m2)	26.4 (7.2)	26.7 (7.4)	0.120
Waist circumference (cm)	93 (20.5)	93.5 (20.8)	0.182
Biochemistry tests
Iron (ug/dL)	73 (39)	74 (39)	0.098
Total iron-binding capacity (ug/dL)	355 (72)	356 (72)	0.450
Transferrin saturation (%)	20.5 (11.1)	20.8 (11.8)	0.329
Ferritin (ng/mL)	87 (125)	84.5 (124)	0.508
Cholesterol (mg/dL)	201 (57)	204 (59)	0.155
Triglyceride (mg/dL)	120 (100.25)	122.5 (102)	0.562
HDL cholesterol (mg/dL)	48 (18)	48.5 (18)	0.585
C-reactive protein (mg/dL)	0.21 (0.29)	0.21 (0.23)	0.686
Uric acid (mg/dL)	5 (1.9)	5.1 (2)	0.427
Liver chemistry
Aspartate aminotransferase (U/L)	19 (8)	19 (7)	0.908
Alanine aminotransferase (U/L)	14 (10)	14 (10)	0.581
Gamma glutamyl transferase (U/L)	21 (18)	21 (18)	0.787
Alkaline phosphatase (U/L)	83 (33)	81 (32)	0.524
Total bilirubin (mg/dL)	0.5 (0.2)	0.5 (0.2)	0.855
Total protein (g/dL)	7.4 (0.6)	7.4 (0.6)	0.559
Albumin (g/dL)	4.1 (0.5)	4.1 (0.4)	0.543
Serum globulin (g/dL)	3.3 (0.6)	3.3 (0.7)	0.941
Diabetes testing profile
Glycated hemoglobin (%)	5.4 (0.8)	5.4 (0.7)	0.075
Fasting plasma glucose (mg/dL)	91.6 (12.52)	92.05 (12.2)	0.726
Fasting C-peptide (pmol/mL)	0.65 (0.68)	0.66 (0.69)	0.746
Fasting insulin (uU/mL)	9.36 (9.51)	9.73 (10.04)	0.378
Diabetes medication	165 (7.28%)	68 (7.01%)	0.782

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.

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.

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.