An Automated Machine Learning Integrated Platform for Real‐Time Quantification of Serum Creatinine in Clinical Samples

ABSTRACT The critical need for rapid, decentralized kidney diagnostics demands a technological leap beyond slow, lab‐bound creatinine assays. We present an intelligent, automated platform that fuses a nanostructured molecularly imprinted polymer (gCN/cPMAA) sensor with embedded machine learning for real‐time serum creatinine quantification. This system transforms raw voltammetry into a robust clinical tool. We engineer six key features from 6000 scans to train ensemble models, with a Random Forest regressor achieving superior accuracy (R 2 = 0.9844). This model is deployed in a standalone desktop application that fully automates the workflow from signal acquisition to report generation. Crucially, validation on 13 unspiked clinical serum samples shows excellent concordance with reference lab values (R 2 = 0.94), significantly outperforming raw sensor readouts. The integrated platform delivers quantitative results in under 5 min, a dramatic acceleration over standard 30+ min methods, by eliminating manual calibration and minimizing user input. This work establishes a scalable, data‐driven framework for point‐of‐care renal diagnostics. Its modular architecture, where both the synthetic receptor and data model can be adapted, provides a foundational technology for intelligent multi‐analyte biosensing in decentralized healthcare.