Unsupervised feature selection using reversed correlation for improved medical diagnosis

Abstract

Statistical inference has been usually used for medical data analysis, however in many cases it appears not to be efficient enough. Cluster analysis enables finding out groups of similar instances, for which statistical models can be built more effectively. In the paper a feature selection method for finding clustering attributes, which are supposed to improve performance of statistical analysis, is proposed. The method consists in selecting reversed correlated features as attributes of cluster analysis. The proposed technique has been evaluated by experiments done on real data sets of cardiovascular cases. Experiment results showed that the presented approach stimulates efficacy of statistical inference applied to medical diagnosis.