# 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.

## Key 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.