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A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia

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  • Faten Al-hussein
  • Mali Abdollahian
  • Laleh Tafakori
  • Khalid Al-Shali

Abstract

Type 2 diabetes (T2D) is considered a significant global health concern. Hemoglobin A1c level (HbA1c) is recognized as the most reliable indicator for its diagnosis. Genetic, family, environmental, and health behaviors are the factors associated with the disease. T2D is linked to substantial economic costs and human suffering, making it a primary concern for health planners, physicians, and those living with the disease. Saudi Arabia currently ranks seventh worldwide in terms of prevalence rate. Despite this high rate, the country lacks focused research on T2D. This study aims to develop hybrid prediction models that integrate the strengths of multiple algorithms to enhance HbA1c prediction accuracy while minimising the number of significant Key Performance Indicators (KPIs). The proposed model can help healthcare practitioners diagnose T2D at an early stage. Analyses were conducted in a case-control study in Saudi Arabia involving cases (patients with HbA1c levels ≥ 6.5) and controls with normal HbA1c levels (

Suggested Citation

  • Faten Al-hussein & Mali Abdollahian & Laleh Tafakori & Khalid Al-Shali, 2025. "A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-26, June.
    • Handle: RePEc:plo:pone00:0326315
    DOI: 10.1371/journal.pone.0326315
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