  In life sciences, the experts generally use empirical knowledge to recode
variables, choose interactions and perform selection by classical approach. The
aim of this work is to perform automatic learning algorithm for variables
selection which can lead to know if experts can be help in they decision or
simply replaced by the machine and improve they knowledge and results. The
Lasso method can detect the optimal subset of variables for estimation and
prediction under some conditions. In this paper, we propose a novel approach
which uses automatically all variables available and all interactions. By a
double cross-validation combine with Lasso, we select a best subset of
variables and with GLM through a simple cross-validation perform predictions.
The algorithm assures the stability and the the consistency of estimators.
