Machine Learning-Assisted Terahertz Metasurface Sensing for Diabetes Progression.

A polarization-insensitive terahertz (THz) metasurface refractive index sensor is proposed for plasma amino acid-based detection of type 2 diabetes progression stages. The unloaded sensor exhibits a resonance frequency of 0.6638 THz at maximum absorptivity, with a high quality factor(Q) of 41.4, a figure of merit(FoM) of 4.17 and maximum refractive index sensitivity(S) of 66.67 GHz/RIU, enabling precise refractive index sensing. The combined effect of six clinically accepted plasma amino acid biomarkers is considered to model diabetes progression. Five progressive stages-Normal (N), Intermediate-1 (I-1), Intermediate-2 (I-2), Diabetic (D), and High-Diabetic (HD)-are characterized by variations in amino acid concentrations that alter the effective refractive index of blood plasma. The Lorenz-Lorentz formulation is employed to estimate stage-dependent refractive indices from molecular composition and density variations. The corresponding resonance shifts and absorptivity responses are obtained through parametric simulations and used to train multiple machine learning models. The proposed framework Ensemble Bagged Trees classifier achieves a classification accuracy of 94% in accurately identifying the precise stage of diabetes on the simulated test datasets. The results demonstrate the potential of integrating THz metasurface sensing with data-driven modeling for minimally invasive, stage-specific diabetes diagnostics.