A Hybrid and Comparative Machine Learning Framework for Predicting Gestational Diabetes Mellitus via a Prospective...

Background/Objectives: This pilot study aimed to develop and compare advanced machine learning models, specifically a Bayesian-regularized artificial neural network (ANN) and a transformer-enhanced physics-informed neural network (PINN), by integrating periodontal health indices and hematological inflammatory markers for the prediction of GDM. Methods: Utilizing a prospective case-control study design, a clinical dataset comprising 80 pregnant women (40 with GDM and 40 healthy controls) was evaluated to develop and compare advanced machine learning models. Clinical, periodontal, and complete blood count-derived inflammatory parameters were integrated into two predictive models: a Bayesian regularization-based artificial neural network (ANN) and a transformer-enhanced physics-informed neural network (PINN). Model performance was evaluated using the coefficient of determination (R²), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and residual error analyses. Results: The ANN demonstrated superior predictive performance, achieving an R² of 0.9872 and an MSE of 3.38 × 10^-3, compared with an R² of 0.9833 for the PINN model. Residual analysis showed that the ANN provided greater prediction stability, with a mean deviation of 0.5691 and a standard deviation of 3.6400. The findings demonstrate that the integrated analysis of periodontal and hematological markers through advanced neural architectures, particularly the Bayesian-regularized ANN, provides a high-fidelity diagnostic signal for GDM prediction. This integrated feature set, when processed by the proposed machine learning framework, enables the identification of complex biological patterns with remarkable precision. Conclusions: The proposed machine learning framework provides an accurate, non-invasive, and clinically applicable approach for predicting GDM. Integrating routinely available periodontal and hematological data may improve early risk stratification and support personalized prenatal care. Further validation in larger and more diverse populations is warranted before clinical implementation.