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

Lasso based feature selection for malaria risk exposure prediction