ZnSe based 2D photonic crystal biosensor supported with machine learning for critical blood component detection and diagnosis of hypofibrinogenemia

• A ZnSe-based two-dimensional photonic crystal biosensor is designed for detecting critical blood components. • The sensor enables identification of conditions such as hypofibrinogenemia, chronic renal disease, and artery-related disorders. • Key performance metrics including wavelength sensitivity, quality factor, and figure of merit are evaluated using FDTD simulations. • A four-cavity layout is investigated to enhance spectral separation between analytes. • Machine learning models (polynomial regression, gradient boost, XGBoost, K-nearest neighbor, Random Forest) are deployed to predict optical parameters. Biosensor for detecting critical blood components using zinc selenide (ZnSe) two dimensional photonic crystal is presented. The proposed sensor is expected to be a resourceful approach in identifying hypofibrinogenemia, chronic renal disease and artery related disorders. Wavelength sensitivity, quality factor (QF) and figure of merit are the defining metrics of the sensor which are being evaluated. Design and implementation of the sensor is done using FDTD (Finite difference time domain) method. Four cavity layout is being investigated in the sensor topology to refine the spectral separation between the analytes. Machine learning (ML) platform is deployed for predicting optical parameters such as wavelength sensitivity and peak wavelength. ML models such as polynomial regression, gradient boost, XGboost regression, K-nearest neighbor regression and Random Forest are leveraged to predict the metrics. R 2 score, Mean squared error (MSE) and mean absolute error (MAE) are evaluated for each model to comprehend the accuracy in predicting sensor metrics.