CA-125 Detection Using Micro-Fluidics With An Implementation of the Latest Machine Learning Algorithms

Electrochemical sensors are very sensitive and accurate detection mechanism for the detection of tumor biomarkers in fluids such as blood, bodily secretions and urine. They detect low analyte concentration very accurately and in real time by electrochemical reaction and low biomolecular interactions. These sensors are vital in oncology for the early detection of cancers in the human body. The forward scan of the cyclic voltammogram is modelled using machine learning algorithms. Machine learning is known to provide an in-depth study of the curve, which cannot be done with a normal human study. Humans can only predict a determined value using small measurements. Still, a machine, when trained, can extract what the time of the reaction was, how fast it occurred, whether it happened successfully or there was an adverse condition at the electrodes, what was the concentration of the sample. The curve provides a detailed breakdown of the entire process and reaction. A machine, when trained in this case and many others, is the key to understanding the inner workings of complex processes. Electrochemical sensors can detect biomarkers with high sensitivity, aiding early cancer diagnosis and treatment monitoring. Tuning with machine learning adds to the overall efficacy and strength in this detection.