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

Table 3 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	15.08	71.69	1
Income	0.14	0	0	0.05
Body fat	3.94	1.9	3.27	3.04
Systolic blood pressure	1.37	0.67	1.01	1.02
Diastolic blood pressure	2.67	0.67	1.8	1.71
Leukocyte	0.72	0.33	4.32	1.79
Hemoglobin	1	0	0	0.33
Platelets	3.64	1.62	0.31	1.86
Fasting plasma glucose	2.92	1.18	0.66	1.59
Total bilirubin	6.86	2.29	40.24	16.46	6
Albumin	1.84	0.54	49.85	17.41	5
Globulin	0.28	0.29	17.83	6.13
Alkaline Phosphatase	0.99	0.15	0	0.38
Serum glutamic oxaloacetic transaminase	1.63	0.36	0	0.66
Serum glutamic pyruvic transaminase	3.83	1.94	0.82	2.20
Serum γ-glutamyl transpeptidase	1.89	1.33	0.48	1.23
Lactate dehydrogenase	23.21	23.25	0.86	15.77
Uric acid	27.03	24.05	60.42	37.17	2
Triglyceride	0.84	0	0.02	0.29
High density lipoprotein cholesterol	1.99	0.8	1.16	1.32
Low density lipoprotein cholesterol	1.78	0.14	0.1	0.67
Calcium	3.97	2.87	65	23.95	4
Phosphorus	0.79	0.36	0	0.38
Thyroid stimulating hormone	6.92	3.99	4.91	5.27
C-reactive protein	0.61	0.22	6.99	2.61
Forced expiratory volume in one second	6.63	3.41	100	36.68	3
Drink area	0.11	0	0.07	0.06
Smoke area	0.25	0	0	0.08
Betel nut area	0	0	0	0.00
Sport area	0.45	0.2	0.28	0.31
Sleep time	0.14	0	0	0.05
Marriage	0	0	1.99	0.66

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