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Machine Learning Framework for HbA1c Prediction: Data Enrichment, Cost Optimization, and Interpretability Through

Mohamed Ezz1, Majed Abdullah Alrowaily1, Menwa Alshammeri1

  • 1Computer Science Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
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This summary is machine-generated.

This study developed an accurate machine learning model to predict glycated hemoglobin (HbA1c) using fewer clinical and demographic factors. This cost-effective approach enhances long-term glycemic control assessment in population health.

Area of Science:

  • Biomarker discovery and prediction
  • Machine learning in healthcare
  • Population health analytics

Background:

  • Glycated hemoglobin (HbA1c) is crucial for monitoring long-term glycemic control.
  • Direct HbA1c testing is often costly and underutilized in large health surveys and resource-limited settings.
  • Existing prediction models for HbA1c often prioritize classification or raw accuracy over interpretability and cost-efficiency.

Purpose of the Study:

  • To develop a highly accurate and interpretable machine learning (ML) model for predicting HbA1c using routinely collected data.
  • To identify a minimal set of cost-effective predictors, reducing reliance on extensive laboratory panels.
  • To establish a transferable and explainable modeling framework applicable to other chronic disease biomarkers.

Main Methods:

Keywords:
HbA1c predictionLightGBMchronic-disease monitoringcost-efficient biomarkersfeature selectioninterpretabilitymachine-learningstratified regression

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  • Utilized data from National Health and Nutrition Examination Survey (NHANES) cycles 2007-2020 (66,148 records, 224 features).
  • Implemented a two-stage feature selection: Incremental Correlation Selection (ICS) followed by Recursive Feature Elimination with Cross-Validation (RFECV).
  • Employed LightGBMRegressor for prediction and assessed interpretability using partial dependence plots and feature importance.

Main Results:

  • The optimal LightGBMRegressor model achieved R² = 0.7161, MAE = 0.334, and MAPE = 5.56% using only 40 selected features.
  • Feature selection significantly reduced the number of required input variables while maintaining robust predictive performance.
  • Interpretability analysis confirmed clinically meaningful relationships between predictors and HbA1c, aligning with physiological understanding.

Conclusions:

  • The proposed framework offers a practical, cost-efficient method for estimating HbA1c with transparent, physiologically coherent insights.
  • This approach enhances scalability for biomarker estimation in population health and clinical decision-support systems.
  • The explainable, efficient, and generalizable design makes it suitable for broad clinical and public health applications.