Development of a blood HbA1c level detection model based on Support Vector Regression (SVR) using microtest data

Glycated hemoglobin (HbAlc) is a key indicator of long-term glycemic control and a marker of diabetes diagnosis. Rapid and cost-effective prediction from microtest data may support screening in resource-limited settings. This study developed and evaluated an HbAlc prediction model using Support Vector Regression (SVR) on small-scale primary microtest data (10 subjects, three repeated sessions) with strict procedures to prevent data leakage. Clinical and biometric numerical variables were standardized and modeled using an SVR with a Radial Basis Function (RBF) kernel. In 5-fold cross-validation, Spearman correlation was applied exclusively to the training data to select the top 10 features per fold, followed by hyperparameter optimization (C, epsilon, gamma) using grid search with cross-validation. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2. The SVR achieved MAE ≈ 0.705, RMSE ≈ 1.285, and R2 ≈ −0.162, indicating performance close to the mean baseline under leakage-free validation. Frequently selected predictors included HbA1c measurements at multiple time points and clinical indicators such as Impaired Glucose Tolerance (IGT). While predictive performance remains limited by sample size, the study establishes a methodologically robust framework for small-scale HbA1c modeling.