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Journal of Automatic Chemistry
Volume 10 (1988), Issue 2, Pages 67-78

Exploring multivariate clinical chemical routine data concerning three major disease groups

1Central Laboratory for Clinical Chemistry, University Hospital Groningen, P.O. Box 30001, Groningen NL-9700 RB, The Netherlands
2Research Group Chemometrics, Pharmaceutical Laboratories, State University of Groningen, A. Deusinglaan 2, Groningen NL-9713 AW, The Netherlands
3Agricultural Mathematics Group, P.O. Box 100, Wageningen NL-6700 AC, The Netherlands

Copyright © 1988 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In preparation for multivariate analysis, an exploratory study has been undertaken to investigate the relative position, separability, homogeneity and shape of three major disease groups, using data from a clinical chemical routine package.

The data set consists of 46 hepatology patients, 50 nephrology patients and 46 cardiology patients, and the measured blood levels include 20 common clinical chemical routine assays. Missing value problems were avoided by deleting some of the variables and objects.

A univariate analysis was used as the basis ofa rescaling of the data.

Bivariate (pairwise) plots of some major assays each show limited separation. The set of three such plots of the three major principal components reveals more distinction between the groups than was offered by univariate analysis. Three-dimensional extensions of these techniques allow better insight than any of the two-dimensional plots, but these three-dimensional versions require more plots for complete interpretation.

Non-linear mapping of the data is the best way of retaining the distances and a fairly good separation is achieved in the plot. The plot is less informative about shape and relative position of the classes.

Representation of the data as pictures of faces does not offer additional information and visual clustering is worse than in any of the techniques mentioned.

During the analysis many assumed properties of the data are confirmed and a good starting pointfor multivariate classification is attained. Easy visual detection of outliers is offered by all techniques. Unfortunately, valuable information is lost in this data set by deleting some incomplete variables.