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An advanced multivariate statistical approach to study coastal sediment data

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Simeonova P., Sarbu C., Spanos T., Simeonov V., Tsakovski S.

Open Chemistry

2006

vol. 4

issue 1

68-80

The present paper deals with the application of classical and fuzzy principal components analysis to a large data set from coastal sediment analysis. Altogether 126 sampling sites from the Atlantic Coast of the USA are considered and at each site 16 chemical parameters are measured. It is found that four latent factors are responsible for the data structure (“natural”, “anthropogenic”, “bioorganic”, and “organic anthropogenic”). Additionally, estimating the scatter plots for factor scores revealed the similarity between the sampling sites. Geographical and urban factors are found to contribute to the sediment chemical composition. It is shown that the use of fuzzy PCA helps for better data interpretation especially in case of outliers.

Determination and modelling of clinical laboratory data of healthy individuals and patients with end-stage renal failure

100%

Papaioannou A., Karamanis G., Rigas I., Spanos T., Roupa Z.

Open Medicine

2009

vol. 4

issue 1

37-48

The analyses of 18 biochemical parameters (alanine aminotransferase, albumin, aspartate aminotransferase, calcium, cholesterol, chloride, creatinine, iron, glucose, γ- glutamyl transferase, alkaline phosphatase, phosphorus, potassium, sodium, total protein, triglycerides, uric acid, and urea nitrogen) were performed for 166 healthy individuals and 108 patients with end-stage renal failure (ESRF). The application of cluster analysis proved that there were points of similarity among all 18 biochemical parameters that formed major groups; these groups corresponded to the authors’ assumption of the existence of several overall patterns of biochemical parameters that may be termed “enzyme-specific”; “general health indicator”; “major component excretion”; “blood-specific indicator”; and “protein-specific”. These patterns also appear in the subsets of males and females that were obtained by separation of the general dataset. In addition, the performance of factor analysis similarly proved the validity of this assumption. This projection and modelling method indicated the existence of seven latent factors, which explained 70.05% of the total variance in the system for healthy individuals and more than 72% of the total variance in the system for patients with ESRF. All these results support the probability that a general health indicator could be constructed by taking into account the existing classification groups in the list of biochemical parameters.

Multivariate statistical interpretation of laboratory clinical data

100%

Papaioannou A., Simeonov V., Plageras P., Dovriki E., Spanos T.

Open Medicine

2007

vol. 2

issue 3

319-334

Laboratory aids are extensively used in the diagnosis of diseases, in preventive medicine, and as management tools. Reference values of clinically healthy people serve as a guide to the clinician in evaluating biochemical parameters. Determination of 21 biochemical parameters of healthy persons using standard methods of analysis. Cluster analysis and principal components analysis were applied on the above 21 biochemical parameters data. The application of a typical classification approach as cluster analysis proved that four major groups of similarity between all 21 clinical parameters are formed, which correspond to the authors assumption of the existence of several summarizing pattern of clinical parameters such as “enzyme,” “major component excretion”, “general health state,” and “blood specific” pattern. These patterns appear also in the subsets obtained by separation of the general dataset into “male”, “female”, “young”, and “adult” healthy groups. The results obtained from principal components analysis have additionally proved the validity of a similar assumption. The intelligent data analysis on the clinical parameter dataset has shown that when a complex system is considered as a multivariate one, the information about the system substantially increases. All these results support an idea that probably a general health indicator could be constructed taking into account the existing classification groups in the list of clinical parameters.

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2 Open Medicine

1 Open Chemistry

1 2009

1 2007

1 2006

3 Spanos T.

2 Papaioannou A.

2 Simeonov V.

1 Dovriki E.

1 Karamanis G.

1 Plageras P.

1 Rigas I.

1 Roupa Z.

1 Sarbu C.

1 Simeonova P.

1 Tsakovski S.

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An advanced multivariate statistical approach to study coastal sediment data

Determination and modelling of clinical laboratory data of healthy individuals and patients with end-stage renal failure

Multivariate statistical interpretation of laboratory clinical data