1. A method, comprising: receiving a diagnostic data set comprising a plurality of observational values for a plurality of diagnostic variables corresponding to an investigated unit and a benchmark unit; determining a plurality of logistic regression coefficients based on the diagnostic data set, each logistic regression coefficient corresponding to at least one diagnostic variable of the plurality of diagnostic variables, wherein determining a plurality of logistic regression coefficients further comprises: separating the diagnostic data set into first and second diagnostic data sets depending on whether the plurality of observational values contained therein correspond to the investigated unit or to the benchmark unit respectively; deriving first and second mean value sets from the first and second diagnostic data sets respectively; determining a covariance matrix between the first and second diagnostic data sets; determining the plurality of logistic regression coefficients based on a product of an inverse of the covariance matrix and a difference between the first and second mean value sets; selecting a subset of most significant diagnostic variables from the plurality of diagnostic variables based on the plurality of logistic regression coefficients in reference to significance criteria, wherein the selecting a subset of most significant diagnostic variables further comprises deriving a plurality of test statistic values comprising t-values and p-values; and generating at least one interaction variable as a cross product between elements of the subset of most significant diagnostic variables.