Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

Table 6 Rank importance of risk factors in model 2, from the most important to the least

Variable	MARS	XGboost	SGB	LightGBM	Catboos	AVG
Uric acid	1	1	1	1	1	1
Age	2	2	2	4	2	2.4
Body mass index	2	5	2	2	3	2.8
Triglyceride	7	4	5	1	5	4.4
Diastolic blood pressure	3	7	3	9	2	4.8
Sport hours	4	6	6	10	4	6
Systolic blood pressure	14	1	4	3	11	6.6
Serum glutamic oxaloacetic transaminase	6	8	8	5	9	7.2
HDL-cholesterol	14	3	7	8	6	7.6
Fasting plasma glucose	14	10	10	6	10	10
Drinking	5	12	12	14	8	10.2
Sleep time	14	9	9	14	7	10.6
Serum glutamic pyruvic transaminase	14	11	11	7	13	11.2
Gender	14	13	14	14	12	13.4
Smoking	14	14	14	14	14	14

AVG: Average of the rank of importance; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; HDL: High-density lipoprotein; SGB: Stochastic gradient boosting; LightGBM: Light Gradient Boosting Machine; CATboost: Categorical Features + Gradient Boosting. Model 2: Model 1 + lifestyle factors.