Graphene-Based Refractive Index Sensor Using Machine Learning for Detection of Mycobacterium Tuberculosis Bacteria

Rapid detection of mycobacterium tuberculosis bacteria is very important in reducing tuberculosis disease. We propose a label-free graphene-based refractive index sensor using a machine learning approach that detects mycobacterium tuberculosis bacteria. The biosensor is designed for higher sensitivity by analyzing different parameters like substrate thickness, resonator thickness, and angle of incidence. Machine learning is applied to predict the values of absorption for different wavelengths. The machine learning model is applied to four different parameters (angle of incidence, substrate thickness, resonator thickness, graphene chemical potential) of the biosensor. The plus shape metasurface is placed above the graphene-SiO2 hybrid layer to improve the sensitivity. The comparative analysis with other published designs is also presented. The proposed sensor with its higher sensitivity and ability to detect mycobacterium tuberculosis bacteria can be used in biomedical devices for diagnostic applications. Experiments are performed to check the K-Nearest Neighbors (KNN)-regressor model's prediction efficiency for predicting absorption values of intermediate wavelengths. Different values of K and two test cases; R-50, U-50 are used to test the regressor models using the R2 Score as an evaluation metric. It is observed from the experimental results that, high prediction efficiency can be achieved using lower values of K in the KNN-Regressor model.