Data dimensionality reduction in anthropometrical investigations.

BACKGROUND Very often it is necessary to make a decision or to establish a diagnosis on the basis of great amounts of different kinds of data. In this paper the principal component analysis procedure was applied to anthropometrical data analysis. OBJECTIVES The aim was to simplify the process of decision making by data dimensionality reduction. A second aim was to check how the reduction affected an analysis of the pubertal growth process. MATERIAL AND METHODS A group of 400 boys was investigated. Three main components were calculated and interpreted. In order to investigate growth changes, the variability of each component was approximated by fourth order polynomials. RESULTS It was shown that the loss of information resulting from data dimensionality reduction is about 25%, so the three calculated principal components contained 75% of the entire information. It seems possible to make an appropriate decision on the basis of that amount of information. CONCLUSIONS The results obtained fully supported using the approach presented for data analysis in the case under consideration.