Ensemble neural network modelling for stratified HbA1c prediction: Integrating past glucose measurement as a predictor of glycaemic control

Accurate assessment of glycemic control is crucial for effective diabetes management and the prevention of long-term complications. This study employed an ensemble neural network framework, combining a Multi-Layer Perceptron Regressor (MLPR) and Classifier (MLPC) model, to predict and stratify HbA1c using routine fasting (FBS) and post-prandial (PPBS) glucose values from retrospective e-laboratory data (n = 22,920, 2021-2024). The regressor, trained on mean FBS and PPBS values from the preceding three months, achieved an R² of 81 ± 3.7%, sMAPE of 9.13 ± 4.01% and RMSE of 1.1 ± 0.01, reflecting high predictive accuracy and minimal bias. Partial Dependence and ICE analyses revealed a strong, consistent positive association of FBS with HbA1c across glycaemic ranges. The classifier, based on predicted HbA1c, achieved 87.4% accuracy, 94.3% precision and a Diagnostic Odds Ratio of 35.26 ± 0.36, as confirmed by ROC analysis, which demonstrated superior discrimination compared to traditional glucose metrics.