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<p>Accurate blood glucose prediction from HbA<sub>1c</sub> is crucial for personalized health monitoring and diabetes management. Existing models often overlook data imbalances and hyperglycemic outliers, reducing accuracy. This study proposes a machine-learning framework to address these challenges. This study investigates a dataset of 197,180 patient samples, focusing on key features such as age, glucose, and HbA<sub>1c</sub> levels. The performance of 42 machine learning models was evaluated using Kernel Density Estimates (KDE) analysis and targeted oversampling to enhance model robustness. A Flask web application has been developed for deployment on the Heroku platform. Results show that the MLP Regressor achieved a moderate R<sup>2</sup> with the raw data. Logarithmic transformation effectively reduced RMSE. Gaussian KDE identified a low-density region around 550&#xa0;mg/dL in hyperglycemia, prompting targeted oversampling. This, combined with logarithmic transformation, resulted in an R<sup>2</sup> of 0.93 and the lowest RMSE with the LGBM model, indicating strong predictive robustness for blood glucose levels. The proposed approach effectively enhances glucose level prediction accuracy in both healthy and diabetic management. The Heroku-deployed web app provides an accessible tool for clinicians and individuals, supporting real-time diabetes management and personalized health monitoring.</p>

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Comprehensive blood glucose level prediction from HbA1c levels using machine learning models across the biological range

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

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  • Tugba Muhlise Okyay,
  • Ibrahim Yilmaz,
  • Macit Koldas

摘要

Accurate blood glucose prediction from HbA1c is crucial for personalized health monitoring and diabetes management. Existing models often overlook data imbalances and hyperglycemic outliers, reducing accuracy. This study proposes a machine-learning framework to address these challenges. This study investigates a dataset of 197,180 patient samples, focusing on key features such as age, glucose, and HbA1c levels. The performance of 42 machine learning models was evaluated using Kernel Density Estimates (KDE) analysis and targeted oversampling to enhance model robustness. A Flask web application has been developed for deployment on the Heroku platform. Results show that the MLP Regressor achieved a moderate R2 with the raw data. Logarithmic transformation effectively reduced RMSE. Gaussian KDE identified a low-density region around 550 mg/dL in hyperglycemia, prompting targeted oversampling. This, combined with logarithmic transformation, resulted in an R2 of 0.93 and the lowest RMSE with the LGBM model, indicating strong predictive robustness for blood glucose levels. The proposed approach effectively enhances glucose level prediction accuracy in both healthy and diabetic management. The Heroku-deployed web app provides an accessible tool for clinicians and individuals, supporting real-time diabetes management and personalized health monitoring.