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Exploring multivariate clinical chemical routine data concerning three major disease groups.

J B Hemel¹, H van der Voet, R Hendriks

¹Central Laboratory for Clinical Chemistry University Hospital Groningen P.O. Box 30001 Groningen NL-9700 RB The Netherlands.

The Journal of Automatic Chemistry

|January 1, 1988

Summary

This study explored clinical chemistry data for three patient groups, finding non-linear mapping best for visualizing disease separability. This approach provides a good starting point for multivariate classification, aiding outlier detection.

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Basics of Multivariate Analysis in Neuroimaging Data

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Published on: July 24, 2010

Area of Science:

Clinical chemistry
Medical data analysis
Multivariate statistics

Background:

Exploratory study of three major disease groups (hepatology, nephrology, cardiology).
Utilized a dataset of 46 hepatology, 50 nephrology, and 46 cardiology patients.
Included 20 common clinical chemical routine assays in the blood level measurements.

Purpose of the Study:

Investigate relative position, separability, homogeneity, and shape of disease groups.
Prepare data for subsequent multivariate analysis and classification.
Evaluate various visualization techniques for clinical chemistry data.

Main Methods:

Data preprocessing involved deleting variables and objects to handle missing values.
Univariate analysis was used for initial data rescaling.
Bivariate plots, principal component analysis (PCA), 3D extensions, and non-linear mapping were employed.
Data visualization techniques including 'pictures of faces' were assessed.

Main Results:

Univariate and bivariate analyses showed limited separation between disease groups.
Principal component analysis (PCA) plots revealed more distinction than univariate analysis.
Three-dimensional techniques offered better insight but required more plots.
Non-linear mapping provided the best distance retention and fair separation, though less informative on shape and position.
All techniques facilitated easy visual outlier detection.

Conclusions:

Non-linear mapping is the most effective visualization for this clinical chemistry dataset.
The study achieved a good starting point for multivariate classification.
Data preprocessing choices led to the loss of valuable information.
Visual detection of outliers is a consistent benefit across methods.