Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver

Table 5 The average of the importance of risk factors derived from stochastic gradient boosting, random forest and extreme gradient boost, in NAFLD+ (Model 2, excluding age)

Variables	SGB	XGBoost	Elasticnet	Average	Rank
Income	12.88	15.99	8.06	12.31
Body fat	22.25	16.83	3.85	14.31	5
Systolic blood pressure	11.29	9.24	0.41	6.98
Diastolic blood pressure	9.17	6.85	1.19	5.74
Leukocyte	1.73	0.58	1.9	1.40
Hemoglobin	2.57	0.31	4.27	2.38
Platelets	17.83	14.87	0.32	11.01
Fasting plasma glucose	9.72	6.71	0.2	5.54
Total bilirubin	9.6	3.56	28.65	13.94
Albumin	12.05	9.81	100	40.62	3
Globulin	1.85	1.25	10.62	4.57
Alkaline Phosphatase	4.28	0.99	0.06	1.78
Serum glutamic oxaloacetic transaminase	3.45	3.05	1.45	2.65
Serum glutamic pyruvic transaminase	16.27	11.92	1.57	9.92
Serum γ-glutamyl transpeptidase	1.14	0.65	0.28	0.69
Lactate dehydrogenase	100	100	0.6	66.87	1
Uric acid	50.16	45.66	30.15	41.99	2
Triglyceride	6.23	3.56	0.14	3.31
High density lipoprotein cholesterol	0.86	0.82	0.05	0.58
Low density lipoprotein cholesterol	9.6	6.73	0.46	5.60
Calcium	12.48	9.07	53.48	25.01	4
Phosphorus	0.79	1.87	1.48	1.38
Thyroid stimulating hormone	16.42	11.23	2.23	9.96
C-reactive protein	0	0	2.11	0.70
Forced expiratory volume in one second	39.39	44.32	38.15	40.62	3
Drink area	0	0	0.26	0.09
Smoke area	0	0	0	0.00
Betel nut area	0	0	0	0.00
Sport area	3.95	3.83	1.49	3.09
Sleep time	0.86	0.5	4.04	1.80
Marriage	0	0	2.38	0.79

SGB: Stochastic gradient boosting; XGBoost: eXtreme gradient boosting; NAFLD: Non-alcoholic fatty liver.