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

Table 2 Comparison with SMAPE, RAE, RRSE, and RMSE between multiple linear regression and machine learning methods

NAFLD+ group with age	MAPE	SMAPE	RAE	RRSE	RMSE
Linear	0.139	0.132	0.845	0.842	13.959
SGB	0.138	0.131	0.841	0.834	13.825
XGBoost	0.139	0.132	0.845	0.842	13.946
Elasticnet	0.139	0.132	0.845	0.842	13.954
NAFLD- group with age
Linear	0.133	0.128	0.868	0.862	14.671
SGB	0.132	0.126	0.855	0.857	14.59
XGboost	0.132	0.126	0.853	0.857	14.58
Elasticnet	0.134	0.128	0.868	0.862	14.673
NAFLD+ group without age
Linear	0.154	0.14	0.872	0.897	15.606
SGB	0.153	0.139	0.865	0.888	15.444
XGboost	0.153	0.14	0.869	0.891	15.49
Elasticnet	0.154	0.14	0.872	0.897	15.596
NAFLD- group without age
Linear	0.134	0.13	0.905	0.906	15.149
SGB	0.133	0.129	0.895	0.892	14.915
XGboost	0.133	0.129	0.895	0.893	14.916
Elasticnet	0.134	0.13	0.904	0.905	15.119

Data showed as mean; SGB: stochastic gradient boosting, XGBoost: eXtreme gradient boosting; RAE: Relative Absolute Error; RRSE: Root Relative Squared Error; RMSE: Root Mean Squared Error; NAFLD: Non-alcoholic fatty liver.