Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection

, and a limit of detection of 0.13 mM. The sensor demonstrated excellent selectivity against common interferences, including urea, lactate, ascorbic acid, uric acid, dopamine, and glucose. Its practical application was demonstrated in urine samples, with results showing strong agreement with the standard creatinine assay. Machine learning models, such as Random Forest, Extra Trees, and XGBoost, were employed to optimize data analysis, delivering high predictive accuracy and uncovering key electrochemical features critical to the sensor's performance.