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

TL;DR

This study uses an ensemble neural network to accurately predict HbA1c levels from glucose measurements, improving diabetes management.

Contribution

The novel approach integrates an MLPR and MLPC model for HbA1c prediction and stratification using routine glucose data.

Findings

The regressor model achieved high accuracy with R2 of 81%, sMAPE of 9.13%, and RMSE of 1.1.

Classifier performance showed 87.4% accuracy and 94.3% precision in HbA1c stratification.

FBS showed a consistent positive association with HbA1c across all glycaemic ranges.

Abstract

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.