Open AccessSERS spectroscopy with machine learning to analyze human plasma derived sEVs for coronary artery disease diagnosis and prognosis
Author(s) -
Huang Xi,
Liu Bo,
Guo Shenghan,
Guo Weihong,
Liao Ke,
Hu Guoku,
Shi Wen,
Kuss Mitchell,
Duryee Michael J.,
Anderson Daniel R.,
Lu Yongfeng,
Duan Bin
Publication year - 2023
Publication title -
bioengineering and translational medicine
Language(s) - English
Resource type - Journals
ISSN - 2380-6761
DOI - 10.1002/btm2.10420
Subject(s) - cad , coronary artery disease , support vector machine , artificial intelligence , myocardial infarction , cardiology , medicine , linear discriminant analysis , machine learning , computer science , engineering , engineering drawing
Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non‐ST‐elevation myocardial infarction, and ST‐elevation myocardial infarction. Surface‐enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K‐Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection.