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Rapid and non-invasive diagnosis of coronary artery disease via clinical laboratory parameters and H-1-NMR spectra of human blood plasma

机译:通过临床实验室参数和人血浆的H-1-NMR光谱快速无创地诊断冠心病

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摘要

Coronary artery disease (CAD), one of the most common fatal diseases in the world, was examined in the present study via the investigation of the H-1-NMR spectra of human blood plasma and clinical laboratory parameters, with the aim of early disease diagnosis. Partial least squares-discriminant analysis (PLS-DA), a common supervised pattern recognition method, assisted by a genetic algorithm (GA) based feature selection procedure, was used to classify CAD(-) and CAD(+) individuals based on spectral patterns and clinical parameters. Meanwhile, unsupervised pattern recognition methods (i.e., hierarchical cluster analysis (HCA) and principal component analysis (PCA)) were implemented to precisely visualize and examine the spectroscopic and clinical datasets. GA and ANOVA techniques were employed to select the discriminant and most effective clinical parameters for recognizing CAD(-) and CAD(+) samples. Finally, the calculated classification models were successfully able to distinguish between CAD(-) and CAD(+) individuals using H-1-NMR spectra and clinical laboratory parameters as a safe, economic, simple and also non-invasive method in comparison with coronary angiography for CAD diagnosis.
机译:冠状动脉疾病(CAD)是世界上最常见的致命疾病之一,本研究通过研究人类血浆的H-1-NMR谱图和临床实验室参数来检查,以期早期发现疾病诊断。偏最小二乘判别分析(PLS-DA)是一种常见的监督模式识别方法,并辅以基于遗传算法(GA)的特征选择程序,用于根据光谱模式对CAD(-)和CAD(+)个体进行分类和临床参数。同时,实施了无监督模式识别方法(即,层次聚类分析(HCA)和主成分分析(PCA))以精确地可视化并检查光谱和临床数据集。利用GA和ANOVA技术选择识别和最有效的临床参数来识别CAD(-)和CAD(+)样本。最后,计算得出的分类模型能够成功地使用H-1-NMR光谱和临床实验室参数来区分CAD(-)和CAD(+)个体,与冠状动脉相比是一种安全,经济,简单且无创的方法血管造影用于CAD诊断。

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