Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk

Table 2 High linear correlation in 16 indicators: Absence of multicollinearity

Collinearity diagnostics
Indicators	Sig.	Tolerance	VIF	n	Correlation coefficient	Correlation ranking
WC	0.790	0.558	1.792	774	0.331	7
Duration of each exercise session	0.724	0.824	1.213	700	-0.172	29
Absolute value of lymphocytes	0.647	0.785	1.274	981	0.186	21
RBC	0.199	0.702	1.425	980	0.173	28
MCHC	0.114	0.831	1.203	889	0.1	71
PCT	0.476	0.750	1.334	889	0.123	48
ALT	0.265	0.554	1.806	979	0.506	1
AST/ALT	0.004	0.532	1.878	831	-0.495	2
GLO	0.360	0.880	1.137	769	0.143	38
TG	0.182	0.874	1.144	917	0.358	4
Cr/weight ratio	0.052	0.606	1.649	770	-0.255	10
HbA1c	0.008	0.758	1.319	495	0.246	13
FPSA	0.187	0.845	1.183	444	-0.194	18
Coating at left side of tongue la	0.694	0.866	1.154	924	-0.139	40
Average heart rate	0.415	0.809	1.235	860	0.233	14
Liver meridian	0.774	0.806	1.240	860	0.178	25

WC: Waist circumference; RBC: Red blood cell count; MCHC: Mean corpuscular hemoglobin concentration; PCT: Procalcitonin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GLO: Globulin; TG: Triglyceride; Cr: Creatinine; HbA1c: Glycated hemoglobin; FPSA: Free prostate-specific antigen.