• Seoane Fernández, José Antonio
• Day, Ian N.M.
• Campbell, Colin
• Casas, Juan Pablo
• Gaunt, Tom R.

In this work we study variable significance in classification using the Random Forest proximity matrix and local importance matrix. We use the proximity matrix to group the samples across a number of clusters and use these clusters to stratify the importance of a variable. We apply this approach to a cardiovascular genotype dataset for sample classification based on coronary heart disease and we found a number of variations related with cardiovascular disease phenotypes. We also used a set of phenotypes related with this genotype data to match the obtained clusters with coronary heart diseases phenotypes.