Artificial Neural Networks Compared to Factor Analysis for Low-Dimensional Classification of High-Dimensional Body Fat Topography Data of Healthy and Diabetic Subjects

Abstract

Subcutaneous adipose tissue thickness was measured in 590 healthy subjects at 15 specific body sites by means of the new optical device, lipometer, providing a high-dimensional and partly highly intercorrelated set of data, which had been analyzed by factor analysis previously. N-2-N back-propagation neural networks are able to perform low-dimensional display of high-dimensional data as a special application. We report about the performance of such a 15-2-15 network and compare its results with the output of factor analysis. As test data for verification, measurement values on women with proven diabetes mellitus type II (NIDDM) are used. Surprisingly our 15-2-15 neural network is able to reproduce the classification pattern resulting from factor analysis very precisely. After extracting the network weights the classification of new subjects is even more simple with the neural network as compared with factor analysis. In addition, the network weights are able to cluster highly correlated body sites nicely to different groups, corresponding to different regions of the human body. Thus, the analysis of these weights provides additional information about the structure of the data. Therefore, N-2-N networks seem to be a good alternative method for analyzing high-dimensional data with strong intercorrelation.