Distance preserving dimension reduction (DPDR) using the singular value decomposition has recently been intro- duced. In this paper, for disease diagnosis using gene or protein expression data, we present empirical comparison results between DPDR and other various dimension reduc- tion (DR) methods (i.e. PCA, MDS, Isomap, and LLE) when using support vector machines with radial basis func- tion kernel. Our results show that DPDR outperforms, as a whole, other DR methods in terms of classification ac- curacy, but at the same time, it gives significant efficiency compared with other methods since it has no parameter to be optimized. Based on these empirical results, we reach a promising conclusion that DPDR is one of the best DR methods at hand for modeling an efficient and distortion- free classifier for gene or protein expression data.