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

Table 3 Results of five different machine learning methods in three different models

Model	Methods	Accuracy	Sensitivity	Specificity	AUC	F1-score	BA
Model 1	MARS	0.689	0.717	0.519	0.633	0.799	0.618
	XGboost	0.715	0.753	0.482	0.602	0.820	0.617
	SGB	0.596	0.590	0.630	0.599	0.715	0.610
	LightGBM	0.731	0.771	0.482	0.615	0.831	0.626
	Catboost	0.549	0.518	0.741	0.623	0.664	0.629
Model 2	MARS	0.689	0.683	0.731	0.693	0.792	0.707
	XGboost	0.632	0.617	0.731	0.663	0.744	0.674
	SGB	0.762	0.802	0.500	0.666	0.854	0.651
	LightGBM	0.767	0.808	0.500	0.646	0.857	0.654
	Catboost	0.637	0.635	0.654	0.663	0.752	0.644
Model 3	MARS	0.767	0.801	0.622	0.760	0.848	0.711
	XGboost	0.762	0.763	0.757	0.786	0.838	0.760
	SGB	0.777	0.789	0.730	0.814	0.851	0.759
	LightGBM	0.741	0.750	0.703	0.776	0.824	0.726
	Catboost	0.819	0.897	0.487	0.744	0.889	0.692

AUC: Area under receiver operating characteristic curve; BA: Balanced accuracy; MARS: Multivariate adaptive regression splines; XGBoost: eXtreme Gradient Boosting; 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.