Analysis of Rehabilitation Data by Multi-Dimensional Principal Component Analysis Method Using the Statistical Software R

Author:

Ishida Akio1,Aibara Ukyo2,Murakami Jun1,Yamamoto Naoki1,Saito Satoko3,Izumi Takeshi3,Kano Nozomi3

Affiliation:

1. Kumamoto National College of Technology

2. University of Ryukyus

3. Kumamoto Rehabilitation Hospital

Abstract

The multi-dimensional principal component analysis (MPCA), which is an extension of the well-known principal component analysis (PCA), is proposed to reduce the dimension and to extract the feature of the multi-dimensional data. We have analyzed the rehabilitation data, which is known as the Functional Independence Measure (FIM), routinely measured from inpatients of a hospital by using MPCA. This time, we implemented the MPCA program by the statistical software R, and carried out the analysis of that data with changed configuration from the previous works in the environment of the R statistical system. From the results, the usefulness and the effectiveness of MPCA analysis in the R environment are confirmed.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference11 articles.

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4. F. Franchignoni, L. Tesio, M.T. Martino, E. Benevolo, and M. Castagna: Length of stay of stroke rehabilitation inpatients: prediction through the functional independence measure, Ann. Ist. Super. Sanita 34 (1998), pp.463-467.

5. N. Yamamoto, J. Murakami, C. Okuma, Y. Shigeto, S. Saito, T. Izumi, and N. Hayashida: Application of multi-dimensional principal component analysis to medical data, Int. J. Eng. Phys. Sci. 6 (2012), pp.260-266.

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