A Wireless and Machine Learning-Based Electrochemical Biosensor Potentiostat System for CD63 Protein Detection in Lung Cancer Biomarkers

Lung cancer remains one of the leading causes of cancer-related deaths globally, with approximately 2.5 million new cases and 1.8 million deaths reported annually according to World Health Organization in 2022. Early and accurate detection is crucial for improving survival rates through timely treatment. However, current detection methods face challenges such as invasiveness, high costs, and delayed diagnosis. Eelectrochemical biosensors, which use potentiostats to detect biological analytes by controlling cell voltage and measuring current, voltage, or impedance, offer a rapid and sensitive alternative. Nevertheless, many existing potentiostats are limited by narrow voltage ranges, single current measurement capabilities, and inadequate support for advanced electrochemical techniques. This study presents a novel wireless electrochemical biosensor potentiostat system enhanced with machine learning and Internet of Things (IoT) integration for the detection of lung cancer biomarkers, specifically the CD63 protein. The system is designed to be portable, energy efficient, cost-effective, and high sensitive, supporting advanced electrochemicals techniques such as cyclic voltammetry (CV) and square wave voltammetry (SWV). It achieves a limit of detection (LoD) of 2 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> particles/mL and a sensitivity of 5.188 μA per log concentration unit, comparable to the commercial systems like uStat8000 potentiostat. Furthermore, the integrated random forest classifier enables fast and automated data interpretation, achieving accuracy rates of 83.5% and 87.5% for CV and SWV cases, respectively. Overall, the developed platform offers a smart, portable, and scalable solution for point-of-care diagnostics, contributing to early detection and improved prognosis in lung cancer management.