Application of biosensing technology in the rapid identification of pathogenic microorganisms

Background: Biosensing technology has developed as a capable tool for the rapid and perfect recognition of pathogenic microorganisms, determining real-time recognition capabilities that are crucial for early disease diagnosis and management. Purpose: This research investigates the integration of biosensors with Machine Learning (ML) techniques for the efficient detection of pathogens. Approaches: Data collection involved using various biosensors, including electrochemical, optical, and mass-based sensors, to capture the microbial signature of different pathogens. The collected data was pre-processed using adaptive filtering (AF) to remove noise and ensure signal clarity. Z-score normalization is utilized to standardize the dataset. Feature extraction was performed using the discrete wavelet transform (DWT) technique to reduce the dimensionality of the data while retaining crucial information. Results: This research proposes an Enhanced Snow Ablation Optimized Adaptive Support Vector Machine (ESAO-ASVM) model designed to enhance the accuracy and effectiveness of classifying complex biological data, such as microbial signatures. The proposed ESAO-ASVM model demonstrated optimal performance with an execution time of 0.58 s, memory usage of 42%, Root mean squared error (RMSE) of 0.01, Mean squared error (MSE) of 0.019, accuracy loss of 0.06, Structural similarity index measure (SSIM) of 80.4, accuracy of 98.5%, precision of 95%, recall of 94%, and an F1-score of 96%. This approach suggests a robust solution for rapidly identifying pathogenic microorganisms, making it an effective clinical diagnostic tool for food safety, and environmental monitoring. Conclusion: The incorporation of Biosensing technology with ML methods contains potential significant improvement in pathogen recognition, enabling faster and more consistent health involvements.