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

Table 1 Comparison of previous machine learning studies for gestational diabetes mellitus prediction[17-28]

Ref.	Sample size	Key predictors	Class imbalance	ML algorithms	Best ML performance	ML explainability	External validation	Calibration analysis
Wu et al[17]	31811	Clinical, biochemical, lipid, thyroid, and obstetric variables; 73-variable model and simplified 7-variable LR model	Not reported	DNN, SVM, KNN, and LR	DNN using 73 variables, AUC: 80%; 7-variable LR, AUC: 77%	Not reported	No	No
Xiong et al[18]	490	Routine blood tests, hepatic and renal function markers, and coagulation markers, particularly PT and aPTT	Not reported	SVM and LightGBM	SVM using PT and aPTT, sensitivity: 88.3%, specificity: 99.47%, and AUC: 94.2%	Not reported	No	No
Kaya et al[19]	97	First-visit venous plasma glucose level, maternal BMI, family history of DM, smoking, and obstetric history	Not reported	Extra trees, average blender, LightGBM, XGBoost, LR, and RF	XGBoost, AUC: 55.0%, accuracy: 66.7%, sensitivity: 80.0%, and specificity: 50.0% in nulliparous women; AUC: 73.3%, accuracy: 72.7%, sensitivity: 40.0%, and specificity: 100.0% in primiparous women	SHAP	No	No
Li et al[20]	7594	Forty-five first-trimester features; top predictors included pre-pregnancy BMI and maternal abdominal circumference at pregnancy initiation, and FPG and HbA1c at the end of the first trimester	Not reported	LR, XGBoost, RF, and other ML algorithms	XGBoost, AUC: 75% at pregnancy initiation and 99% at the end of the first trimester in the XHCM cohort; external validation AUC: 83% in the SPNPH cohort	Feature importance analysis	Yes	No
Zorlu et al[21]	400	Maternal characteristics, BMI, PAPP-A, and free β-hCG	Not reported	RF, GradBoost, and LR	GradBoost, AUC: 71.5% and accuracy: 71.3%	Not reported	No	No
Ni et al[22]	956	Common first-trimester clinical and laboratory variables selected by Spearman correlation analysis and Boruta algorithm, including pre-pregnancy BMI, SBP, and HDL-C	Not reported	LR, RF, XGBoost, LightGBM, MLP, KNN, and SVM	LR, AUC: 78.7% (95%CI: 72.3%-85.0%); RF, AUC: 77.6% (95%CI: 71.1%-84.1%)	Not reported	No	Yes
Zaky et al[23]	138	History of high glucose/diabetes, insulin, HOMA-IR, uric acid, cholesterol, urea, PT, NT-proBNP, thyroid markers, and routine blood markers	Not reported	RF, GradBoost, AdaBoost, DT, LR, SVM, Gaussian NB, KNN, CatBoost, XGBoost, LightGBM, and stacking ensemble	Stacking ensemble, accuracy: 88.8%, precision: 87.3%, sensitivity: 92.1%, and F1 score: 89.6%	SHAP	No	No
Bigdeli et al[24]	106	Age, BMI, previous abortion history, FPG, demographic variables, medical history, and clinical findings	SMOTE	DT, MLP, KNN, NB, RF, and XGBoost	RF, accuracy: 89%, precision: 86%, sensitivity: 92%, and AUC: 94%	Not reported	No	No
Pazaras et al[25]	797	Maternal demographics, obstetric history, lifestyle factors, and FFQ-derived dietary micronutrient intake	SMOTE, borderline SMOTE, adaptive synthetic sampling, and SMOTE-Tomek	LR, RF, extra trees, XGBoost, LightGBM, CatBoost, GradBoost, AdaBoost, and MLP	LR without resampling, AUC: 66.4% (95%CI: 54.2%-77.7%), sensitivity: 78.3%, and NPV: 93.2%; reduced 9-feature model, AUC: 71.2% (95%CI: 58.9%-82.5%)	SHAP	No	Yes
Prashanthan and Prashanthan[26]	10000	Synthetic demographic characteristics, clinical risk factors, and first-trimester laboratory parameters including random blood sugar, post-prandial blood sugar, HbA1c, and OGTT values	SMOTE	Multiple ML algorithms	Best model, accuracy: 71.7% and AUC: 76.9%	SHAP	No	No
Zhai et al[27]	534	BMI, SAT, VAT, maternal clinical characteristics, and first-trimester ultrasonographic indicators	IPW	XGBoost, ANN, SVM, MLR, and RF	XGBoost with GA-selected features, internal test AUC: 96.2%; external validation AUC: 87.8%, sensitivity: 70.0%, and specificity: 93.5%	GA-based feature selection and heatmaps	Yes	No
Louzoun et al[28]	596 twin pregnancies	WBC count, platelet levels, BMI, and previous GDM	Not reported	LightGBM, XGBoost, and LR	LightGBM, AUC: 72% (95%CI: 69%-75%); detection rates: 28% and 42% at false-positive rates of 10% and 20%, respectively	Not reported	No	No
Present study	2756	Maternal age, MAP, pregestational weight, PAPP-A, and PlGF	SMOTE, ROS, and RUS	LR, GradBoost, XGBoost, AdaBoost, LightGBM, SVM, RF, and MLP	GradBoost + ROS, AUC: 76.8%, sensitivity: 64.7%, specificity: 77.7%, PPV: 28.9%, NPV: 94.0%, and F1 score: 40.0%	SHAP and patient-level heatmaps	No	Yes

ML: Machine learning; LR: Logistic regression; PT: Prothrombin time; aPTT: Activated partial thromboplastin time; BMI: Body mass index; DM: Diabetes mellitus; FPG: Fasting plasma glucose; HbA1c: Glycated hemoglobin; PAPP-A: Pregnancy-associated plasma protein A; β-hCG: β-human chorionic gonadotropin; SBP: Systolic blood pressure; HDL-C: High-density lipoprotein cholesterol; HOMA-IR: Homeostatic model assessment for insulin resistance; NT-proBNP: N-terminal pro-B-type natriuretic peptide; FFQ: Food frequency questionnaire; OGTT: Oral glucose tolerance test; SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue; WBC: White blood cell count; GDM: Gestational diabetes mellitus; MAP: Mean arterial pressure; PlGF: Placental growth factor; IPW: Inverse probability weighting; SMOTE: Synthetic minority oversampling technique; RUS: Random undersampling; ROS: Random oversampling; DNN: Deep neural network; SVM: Support vector machine; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine; XGBoost: Extreme gradient boosting; RF: Random forest; GradBoost: Gradient boosting; MLP: Multilayer perceptron; AdaBoost: Adaptive boosting; DT: Decision tree; Gaussian NB: Gaussian naïve Bayes; CatBoost: Categorical boosting; NB: Naïve Bayes; ANN: Artificial neural network; MLR: Multivariate logistic regression; CI: Confidence interval; AUC: Area under the receiver operating characteristic curve; NPV: Negative predictive value; GA: Genetic algorithm; SHAP: SHapley Additive exPlanations; PPV: Positive predictive value.

Table 2 Baseline characteristics of the study population, n (%)/median (interquartile range)

Variables	GDM (n = 352)	Non-GDM (n = 2404)	P value
Age (years)	34.0 (31.0-37.0)	33.0 (30.0-35.0)	< 0.001a
Pregestational BMI (kg/m2)	22.9 (20.7-27.1)	21.5 (19.6-23.9)	< 0.001a
Previous GDM	30 (8.5)	19 (0.8)	< 0.001a
Previous gestational hypertension	2 (0.6)	11 (0.5)	0.671
Previous preeclampsia	5 (1.4)	17 (0.7)	0.181
Family history of diabetes mellitus	30 (8.5)	182 (7.6)	0.561
Previous macrosomia	6 (1.7)	7 (0.3)	0.004a
PCOS history	0 (0)	9 (0.4)	0.613
IVF	48 (13.6)	221 (9.2)	0.013a
Chronic hypertension	11 (3.1)	17 (0.7)	< 0.001a
Cardiovascular disease	4 (1.1)	1 (< 0.1)	0.001a

^aP < 0.05.

GDM: Gestational diabetes mellitus; BMI: Body mass index; PCOS: Polycystic ovary syndrome; IVF: In vitro fertilization.

Table 3 First-trimester clinical, ultrasound, and biochemical parameters of the study population, median (interquartile range)

Variables	GDM (n = 352)	Non-GDM (n = 2404)	P value
GA at scan (weeks)	12.7 (12.4-13.0)	12.7 (12.4-13.0)	0.277
CRL (mm)	66.6 (62.4-70.5)	66.6 (62.9-70.4)	0.385
NT (mm)	1.8 (1.7-2.0)	1.8 (1.7-2.0)	0.879
MAP (mmHg)	84.7 (77.3-93.0)	81.7 (75.7-88.0)	< 0.001a
PAPP-A (IU/L)	5.07 (3.26-6.96)	5.80 (4.04-8.11)	< 0.001a
PlGF (pg/mL)	41.80 (29.66-53.26)	43.00 (31.31-57.00)	0.049a
Free β-hCG (IU/L)	38.60 (25.80-58.80)	41.40 (28.60-61.00)	0.020a
Uterine artery PI	1.60 (1.32-1.97)	1.58 (1.31-1.89)	0.072

^aP < 0.05.

GDM: Gestational diabetes mellitus; GA: Gestational age; CRL: Crown-rump length; NT: Nuchal translucency; MAP: Mean arterial pressure; PAPP-A: Pregnancy-associated plasma protein A; PlGF: Placental growth factor; β-hCG: β-human chorionic gonadotropin; PI: Pulsatility index.

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.