Multivariate statistical interpretation of laboratory clinical data

Abstract

AbstractLaboratory 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.