Establishment of clinical diagnostic models using glucose, lipid, and urinary polypeptides in gestational diabetes mellitus

BACKGROUND: Gestational diabetes mellitus (GDM) has many adverse outcomes that seriously threaten the short‐term and long‐term health of mothers and infants. This study comprehensively analyzed the clinical diagnostic value of GDM‐related clinical indexes and urine polypeptide research results, and established comprehensive index diagnostic models. METHODS: In this study, diagnostic values from the clinical indexes of serum triglyceride (TRIG), high‐density lipoprotein cholesterol (HDL‐C), fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c), and 7 GDM‐related urinary polypeptides were analyzed retrospectively. The multiple logistic regression equation, multilayer perceptron neural network model, radial basis function, and discriminant analysis function models of GDM‐related indexes were established using machine language. RESULTS: The results showed that HbA1c had the highest diagnostic value for GDM, with an area under the curve (AUC) of 0.769. When the cut‐off value was 4.95, the diagnostic sensitivity and specificity were 70.5% and 70.0%, respectively. Among the seven GDM‐related urinary polypeptides, human hemopexin (HEMO) had the highest diagnostic value, with an AUC of 0.690. When the cut‐off value was 368.5, the sensitivity and specificity were 79.5% and 43.3%, respectively. The AUC of the multilayer perceptron neural network model was 0.942, followed by binary logistic regression (0.938), radial basis function model (0.909), and the discriminant analysis function model (0.908). CONCLUSION: The establishment of a GDM diagnostic model combining blood glucose, blood lipid, and urine polypeptide indexes can lay a foundation for exploring machine language and artificial intelligence in diagnostic systems.