Machine Learning (ML) is a powerful tool for big data analysis that shows substantial potential in the field of healthcare. Individual patient data can be inundative, but its value can be extracted by ML's predictive power and ability to find trends. A great area of interest is early diagnosis and disease management strategies for cardiovascular disease (CVD), the leading cause of death in the world. Treatment is often inhibited by analysis delays, but rapid testing and determination can help improve frequency for real time monitoring. In this research, an ML algorithm was developed in conjunction with a flexible BNP sensor to create a quick diagnostic tool. The sensor was fabricated as an ion-selective field effect transistor (ISFET) in order to be able to quickly gather large amounts of electrical data from a sample. Artifical samples were tested to characterize the sensors using linear sweep voltammetry, and the resulting data was utilized as the initial training set for the ML algorithm, an implementation of quadratic discriminant analysis (QDA) written in MATLAB. Human blood serum samples from 30 University of Pittsburgh Medical Center (UPMC) patients were tested to evaluate the effective sorting power of the algorithm, yielding 95% power in addition to ultra fast data collection and determination.

机器学习（ML）是用于大数据分析的强大工具，它显示了医疗领域的巨大潜力。个别患者数据可能具有泛滥性，但其价值可以通过ML的预测能力和发现趋势的能力来提取。引起人们极大兴趣的是心血管疾病（CVD）的早期诊断和疾病管理策略，心血管疾病是世界上主要的死亡原因。分析延迟通常会抑制治疗，但是快速测试和确定有助于提高实时监测的频率。在这项研究中，开发了一种ML算法以及一个灵活的BNP传感器来创建快速诊断工具。该传感器被制造为离子选择场效应晶体管（ISFET），以便能够快速从样品中收集大量电数据。使用线性扫描伏安法测试了人工样本以表征传感器，并将所得数据用作ML算法的初始训练集，该算法是用MATLAB编写的二次判别分析（QDA）的实现。测试了来自匹兹堡大学医学中心（UPMC）的30名患者的人血清样本，以评估该算法的有效分类能力，除超快速的数据收集和确定外，还可以产生95％的功效。