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Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed linear separability ( RLS ) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic) clinical data of patients with end-stage renal disease. The  RLS  method allowed for substantial reduction of the dimensionality through omitting redundant features while maintaining the linear separability of data sets of patients with high and low levels of an inflammatory biomarker. The synergy between genetic and phenotypic features in differentiation between these two subgroups was demonstrated.

Selection of Genetic and Phenotypic Features Associated with Inflammatory Status of Patients on Dialysis Using Relaxed Linear Separability Method