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.


OBJECTIVE
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.


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.
Institute of Computer Technology, Automatics and Robotics, Wroclaw University of Technology, Wrocław, Poland. henryk.kordecki@pwr.wroc.pl
Henryk
H
Kordecki
Maria
M
Knapik-Kordecka
Mikołaj
M
Karmowski
Bohdan
B
Gworys
Andrzei
A
Karmowski
D000293:Adolescent; D000367:Age Factors; D000886:Anthropometry; D001827:Body Height; D049628:Body Size; D002648:Child; D003627:Data Interpretation, Statistical; D006801:Humans; D008297:Male; D015233:Models, Statistical; D025341:Principal Component Analysis; D011627:Puberty; D012885:Skinfold Thickness; D055105:Waist Circumference