Observations about the Principal Components Analysis and Data Clustering Techniques in the Study of Medical Data

The medical data statistical analysis often requires the using of some special techniques, because of the particularities of these data. The principal components analysis and the data clustering are two statistical methods for data mining very useful in the medical field, the first one as a method to decrease the number of studied parameters, and the second one as a method to analyze the connections between diagnosis and the data about the patient-s condition. In this paper we investigate the implications obtained from a specific data analysis technique: the data clustering preceded by a selection of the most relevant parameters, made using the principal components analysis. Our assumption was that, using the principal components analysis before data clustering - in order to select and to classify only the most relevant parameters – the accuracy of clustering is improved, but the practical results showed the opposite fact: the clustering accuracy decreases, with a percentage approximately equal with the percentage of information loss reported by the principal components analysis.

Authors:

• Cristina G. Dascâlu
• Corina Dima Cozma
• Elena Carmen Cotrutz

Keywords:

• Data clustering
• medical data
• principal components analysis.

References:

[1] Chernick, M.R., Friis, R.H., Introductory Biostatistics for the Health
Sciences, John Wiley & Sons Publ., 2003.
[2] Zhou, X.H., Obuchowski, N.A., McClish, D.K., Statistical Methods in
Diagnostic Medicine, John Wiley & Sons Publ., 2002.
[3] Saporta, G., ┼×tefânescu, M.V., Analiza datelor ┼ƒi informaticâ, Ed.
Economicâ, 1996 (in romanian).
[4] C. Dascâlu, Boiculese, L., "The Usefulness of Algorithms Based on
Clustering in the Diagnosis Finding in Medical Practice", in Lecture
Notes of the ICB Seminars - Statistics and Clinical Practice, editors: L.
Bobrowski, J. Doroszewski, E. Marubini, N. Victor, Warsaw, 2000, pg.
53 - 56.
[5] Alsabti, K., Ranka, S., Singh, V., "An Efficient K-Means Clustering
Algorithm", in Proceedings of the 1st Workshop on High-Performance
Data Mining, 1998.
[6] Dumitrescu, D., Teoria clasificârii, Babe┼ƒ - Bolyai University, Cluj -
Napoca, 1991 (in romanian).