In this paper novel theoretical medical decision support system based on a personalized modeling gene selection method is presented. Identifying a compact set of genes from gene expression data is a critical step in bioinformatics research. Personalized modeling is a recently introduced technique for constructing clinical decision support systems. In this work we have provided a comparative study using the proposed Personalized Modeling based Gene Selection method on two benchmark microarray datasets (Colon cancer and Central Nervous System cancer data) and on a macroarray dataset collected during a GRANT clinical project. The experimental results show that our method is able to identify a small number of informative genes which can lead to reproducible and acceptable predictive performance without expensive computational cost. These genes are of importance for specific groups of people for cancer and other diseases diagnosis and prognosis.