Machine Learning Approaches in Optical Sensing for Precision Kidney Function Analysis

Optical sensing presents a viable approach to non-invasive kidney function assessment, mitigating the drawbacks of invasive methods, including discomfort and cost. The study presents a unique optical sensing device that measures key kidney function parameters such as urea, glucose, creatinine, and uric acid using infrared LEDs and photodiodes. Through the utilization of these chemicals' distinct absorption spectra, the proposed system provides very accurate and precise real-time monitoring. The results show a significant improvement over current invasive procedures, with performance measures exceeding those of traditional approaches, and error margins cut in half. Regression analysis yields an R-squared value of 0.98, a Bland-Altman bias of only 0.1 mg/dL, and a Pearson's correlation coefficient of 0.95, demonstrating the proposed system's dependability and precision in clinical applications. These results from validation testing attest to the proposed system's efficacy and reliability. Overall, the study highlights how optical sensing technology can revolutionize kidney function analysis by providing a non-invasive, economical, and effective substitute for traditional techniques.