A feasibility study of diagnosing cardiovascular diseases based on blood/urine element analysis and consensus models

The classification of normal and cardiovascular disease groups with consensus models according to metal concentration in blood/urine samples is discussed in this study. The concentrations of nine elements (i.e., chromium, iron, manganese, aluminum, cadmium, copper, zinc, nickel and selenium) were analyzed using three types of chemometric methods including fisher linear discriminant analysis (FLDA), support vector machine (SVM) and decision tree (DTree). Data from 60 healthy individuals and 24 cardiovascular patients were collected and analyzed. Principal component analysis (PCA) was initially used in a preliminary analysis; however, it proved a difficult task to distinguish normal samples from cardiovascular ones using this method. Then, based on the consensus strategy, a series of classifiers were constructed and compared. In terms of three performance indices, i.e., accuracy, sensitivity and specificity, the DTree classifier exhibited the best overall performance, followed by SVM and FLDA is the poorest. In addition, analysis of blood samples was superior to urine samples. In conclusion, the combination of a consensus DTree classifier and elemental analysis of blood samples can serve as an aid for diagnosis of cardiovascular diseases, especially in routine physical examination.