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

Table 5 Rank importance of risk factors in model 1, from the most important to the least

Variable	MARS	XGboost	SGB	LightGBM	Catboost	AVG
Uric acid	1	1	1	1	1	1
Age	1	2	2	2	2	1.8
Body mass index	11	3	8	4	3	5.8
HDL-cholesterol	11	4	4	9	5	6.6
Systolic blood pressure	11	6	5	5	7	6.8
Serum glutamic pyruvic transaminase	4	9	7	10	4	6.8
Fasting plasma glucose	5	10	10	3	9	7.4
Diastolic blood pressure	11	5	6	8	8	7.6
Gender	3	8	11	7	10	7.8
Triglyceride	11	11	3	6	11	8.4
Serum glutamic oxaloacetic transaminase	11	7	9	11	6	8.8

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 1: Demographic and biochemistry data; Model 2: Model 1 + lifestyle factors; Model 3: Model 2 + inflammation factors.