Abstract
Coronary heart disease (CHD) is one of the primary causes of death globally. There are several diagnostic techniques for CHD at present, but they are invasive and with limited accuracy. In the work, measurement of human urine based on surface-enhanced Raman spectroscopy (SERS) was proposed to diagnose CHD. Urine samples of 157 CHD patients and 63 healthy controls (HC) were investigated by SERS. Statistical analysis of the measured data was then performed. It was found that there were intensity differences in nine Raman peaks (1223/1243/1272/1463/1481/1516/1536/1541/1550 cm−1) between CHD and HC in their average SERS spectrum. Furthermore, principal component analysis (PCA)-linear discriminant analysis (LDA) was then utilized to establish a prediction model to classify CHD and HC. It revealed that the accuracy, specificity and sensitivity of the prediction model validated by leave-one-patient-out cross validation (LOPOCV) were 84.09%, 92.06% and 80.89%, respectively. Therefore, the proposed method can be employed as a non-invasive, rapid and accurate tool for CHD diagnosis in clinical application.
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