Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters

Table 4 Performance metrics of different machine learning models

Sampler	Model	AUC-ROC	Sensitivity	Specificity	PPV	NPV	F1 score	Accuracy
Original	LogReg	0.676 (0.565-0.784)	0.147	0.992	0.714	0.892	0.244	0.888
	XGBoost	0.720 (0.618-0.808)	0.118	0.988	0.571	0.888	0.195	0.880
	AdaBoost	0.674 (0.548-0.792)	0.147	0.975	0.455	0.891	0.222	0.873
	GradBoost	0.731 (0.621-0.832)	0.147	0.983	0.556	0.891	0.233	0.880
	LightGBM	0.702 (0.600-0.795)	0.118	0.988	0.571	0.888	0.195	0.880
	SVM	0.703 (0.592-0.808)	0.088	1.000	1.000	0.886	0.162	0.888
	RandForest	0.713 (0.605-0.813)	0.029	0.996	0.500	0.880	0.056	0.877
	MLP	0.639 (0.531-0.740)	0.147	0.938	0.250	0.887	0.185	0.841
SMOTE	LogReg	0.675 (0.562-0.787)	0.559	0.591	0.161	0.905	0.250	0.587
	XGBoost	0.694 (0.588-0.795)	0.235	0.979	0.615	0.901	0.340	0.888
	AdaBoost	0.674 (0.564-0.783)	0.353	0.901	0.333	0.908	0.343	0.833
	GradBoost	0.659 (0.553-0.762)	0.206	0.971	0.500	0.897	0.292	0.877
	LightGBM	0.696 (0.597-0.783)	0.059	0.979	0.286	0.881	0.098	0.866
	SVM	0.675 (0.564-0.777)	0.618	0.711	0.231	0.930	0.336	0.699
	RandForest	0.708 (0.611-0.803)	0.147	0.983	0.556	0.891	0.233	0.880
	MLP	0.632 (0.531-0.736)	0.265	0.905	0.281	0.898	0.273	0.826
ROS	LogReg	0.677 (0.565-0.789)	0.588	0.587	0.167	0.910	0.260	0.587
	XGBoost	0.701 (0.605-0.790)	0.176	0.926	0.250	0.889	0.207	0.833
	AdaBoost	0.715 (0.602-0.819)	0.588	0.752	0.250	0.929	0.351	0.732
	GradBoost	0.768 (0.679-0.849)	0.647	0.777	0.289	0.940	0.400	0.761
	LightGBM	0.720 (0.620-0.811)	0.206	0.975	0.538	0.897	0.298	0.880
	SVM	0.707 (0.591-0.813)	0.618	0.723	0.239	0.931	0.344	0.710
	RandForest	0.724 (0.618-0.823)	0.088	0.996	0.750	0.886	0.158	0.884
	MLP	0.610 (0.504-0.723)	0.235	0.909	0.267	0.894	0.250	0.826
RUS	LogReg	0.680 (0.567-0.789)	0.559	0.624	0.173	0.910	0.264	0.616
	XGBoost	0.736 (0.642-0.821)	0.706	0.640	0.216	0.939	0.331	0.649
	AdaBoost	0.662 (0.565-0.761)	0.647	0.612	0.190	0.925	0.293	0.616
	GradBoost	0.717 (0.614-0.816)	0.706	0.628	0.211	0.938	0.324	0.638
	LightGBM	0.737 (0.629-0.826)	0.735	0.570	0.194	0.939	0.307	0.591
	SVM	0.708 (0.594-0.805)	0.706	0.607	0.202	0.936	0.314	0.620
	RandForest	0.740 (0.640-0.828)	0.706	0.616	0.205	0.937	0.318	0.627
	MLP	0.643 (0.524-0.753)	0.676	0.525	0.167	0.920	0.267	0.543

AUC-ROC: Area under the receiver operating characteristic curve; NPV: Negative predictive value; PPV: Positive predictive value; SMOTE: Synthetic minority oversampling technique; RUS: Random undersampling; ROS: Random oversampling; LogReg: Logistic regression; XGBoost: Extreme gradient boosting; AdaBoost: Adaptive boosting; GradBoost: Gradient boosting; LightGBM: Light gradient boosting machine; SVM: Support vector machine; RandForest: Random forest; MLP: Multilayer perceptron.