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Papaioannou A.,Education and Technological Institute of Larissa | Rigas G.,Education and Technological Institute of Larissa | Plageras P.,Education and Technological Institute of Larissa | Karikas G.A.,Technological Educational Institute of Athens | Karamanis G.,Diagnostic Laboratories
Journal of Clinical Laboratory Analysis | Year: 2013

Background: In recent years, the use of biochemical markers has received increasing attention for purposes of risk assessment and clinical management in renal failure patients. Chemometric methods are often used in medical studies and there are already indications for their specific role as a tool of the medical statistics. Methods: Three chemometric methods, discriminant analysis (DA), binary logistic regression analysis (BLRA), and cluster analysis (CA), were used for assessment and modeling of routinely used biochemical laboratory data of 18 parameters that were determined from 185 healthy individuals (HIs) and 173 end-stage renal failure (ESRF) patients. Results: The above-mentioned chemometric methods were performed using the data set of 14 parameters since the rest 4 parameters did not present significant difference between healthy and patients. DA created a model using only ALB (Albumin), K (Potassium), TG (Triglyceride), and ALP (Alkaline phosphatase); BLRA model also used the above four parameters; CA classified all the cases into two clusters using the same four parameters and one more parameter, AST (aspartate aminotransferase). Conclusions: This study provides models for assessment and modeling of routinely used biochemical laboratory data, finding groups of similarity among clinical tests usually determined on HIs and ESRF patients, contributing in data mining and reducing costs. © 2013 Wiley Periodicals, Inc. Source


Papaioannou A.,Education and Technological Institute of Larissa | Dovriki E.,Education and Technological Institute of Larissa | Rigas N.,Education and Technological Institute of Larissa | Plageras P.,Education and Technological Institute of Larissa | And 3 more authors.
Water Resources Management | Year: 2010

Various chemometric methods were used to analyze and model potable water quality data. Twenty water quality parameters were measured at 164 different sites in three representative areas (low land, semi-mountainous, and coastal) of the Thessaly region (Greece), for a 3-month period (September to November 2006). Hierarchical cluster analysis (CA) grouped the 164 sample sites into two clusters (CA-group 1 and CA-group 2) based on the similarities of potable water quality characteristics. Discriminant analysis was assigned about 94.5% of the cases grouped by CA. Factor analysis (FA) was applied to standardized log-transformed data sets to examine the differences between the above clusters and identify their latent factors. For each of the above two clusters (CA-group 1 and CA-group 2), FA yielded six latent factors that explain 68.7% and 73.4% of the total variance, respectively. FA was also identified the latent factors that characterize each cluster. The identification was obtained, using (a) descriptive statistics, (b) t test for equality of cluster means, (c) box plot, (d) error bar, (e) factors score plots, (f) matrix scatter score means plot and (g) scatter plot of the six significant latent factors from the factor set of all samples group. The classification scheme obtained through cluster analysis was confirmed by discriminant analysis and explained by factor analysis. © 2010 Springer Science+Business Media B.V. Source

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