title: A feasibility study of diagnosing cardiovascular diseases based on blood/urine element analysis and consensus models.
abstract: 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.
authors.affiliation: Hospital, Yibin University, Yibin, Sichuan 644007, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Chen
authors.forename: Chao
authors.initials: C
authors.lastname: Tan
authors.forename: Zan
authors.initials: Z
authors.lastname: Lin
authors.forename: Tong
authors.initials: T
authors.lastname: Wu
authors.forename: Yuanbo
authors.initials: Y
authors.lastname: Diao
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000097:blood* / Q000175:diagnosis / Q000453:epidemiology / Q000652:urine*; D016022:Case-Control Studies; D019295:Computational Biology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D019216:Metals, Heavy / Q000097:blood* / Q000652:urine*; D008875:Middle Aged; D008955:Models, Cardiovascular; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D019216:Metals, Heavy