A logistic regression (LR) is a simple classification statistical model that provides a binary response to the distribution of the input data among a specific descriptor. The simplest regressions fit the distributions of data to a sigmoidal function, where the input values are given a probability value, which is then classified into one of the two classes based on a cut-off value. We firstly performed a feature selection process specific for logistic regressions by using the 'bestglm' package in R 123 which evaluates through n iterations, which combination of descriptors gives the best fitted regression through the leaps algorithm. This package evaluates the weight of each descriptor by linearizing the sigmoidal function and giving a slope value and standard error for each parameter like a multiple linear regression model (Equation ).