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

Table 4 The average of the importance of risk factors derived from stochastic gradient boosting, random forest and extreme gradient boost, in NAFLD- (Model 1, including age)

Variables	SGB	XGBoost	Elasticnet	Average	Rank
Age	100	100	15.19	71.73	1
Income	0	0	1.41	0.47
Body fat	3.69	1.05	4.15	2.96
Systolic blood pressure	0.46	0.09	0.75	0.43
Diastolic blood pressure	4.19	3.09	2.96	3.41
Leukocyte	1.21	0.34	4.52	2.02
Hemoglobin	4.81	1	11.57	5.79
Platelets	2.61	1.06	0.2	1.29
Fasting plasma glucose	0.42	0.21	0.17	0.27
Total bilirubin	3.11	1.84	19.24	8.06
Albumin	2.28	1.34	69.53	24.38	4
Globulin	0.42	0.12	3.03	1.19
Alkaline Phosphatase	1.84	0.22	0.04	0.70
Serum glutamic oxaloacetic transaminase	0.52	0	0	0.17
Serum glutamic pyruvic transaminase	3.79	1.94	1.12	2.28
Serum γ-glutamyl transpeptidase	1.16	0	0.38	0.51
Lactate dehydrogenase	21.99	18.24	0.97	13.73	5
Uric acid	26.62	22.99	76.35	41.99	2
Triglyceride	1.59	0.31	0	0.63
High density lipoprotein cholesterol	1.4	0.25	0.65	0.77
Low density lipoprotein cholesterol	2.37	0.26	0.18	0.94
Calcium	1.66	0.64	29.96	10.75	6
Phosphorus	2.05	2.07	10.98	5.03
Thyroid stimulating hormone	11.86	8.69	5.02	8.52
C-reactive protein	0.42	0	2.73	1.05
Forced expiratory volume in one second	5.06	3.01	100	36.02	3
Drink area	0	0.25	0	0.08
Smoke area	0.37	0.17	0.79	0.44
Betel nut area	0	0	0	0.00
Sport area	1.13	0.65	1.55	1.11
Sleep time	0	0	0	0.00
Marriage	0.13	0	5.51	1.88

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