Sélection de variables par le GLM-Lasso pour la prédiction du risque palustre
This work addresses the need for automated variable selection in epidemiology to reduce reliance on expert knowledge, though it is incremental as it applies existing Lasso and GLM techniques to a specific domain.
The study tackled the problem of variable selection for malaria risk prediction by proposing an automatic method using Lasso and GLM to handle high-dimensional data without expert preprocessing, resulting in identification of key climatic and environmental factors.
In this study, we propose an automatic learning method for variables selection based on Lasso in epidemiology context. One of the aim of this approach is to overcome the pretreatment of experts in medicine and epidemiology on collected data. These pretreatment consist in recoding some variables and to choose some interactions based on expertise. The approach proposed uses all available explanatory variables without treatment and generate automatically all interactions between them. This lead to high dimension. We use Lasso, one of the robust methods of variable selection in high dimension. To avoid over fitting a two levels cross-validation is used. Because the target variable is account variable and the lasso estimators are biased, variables selected by lasso are debiased by a GLM and used to predict the distribution of the main vector of malaria which is Anopheles. Results show that only few climatic and environmental variables are the mains factors associated to the malaria risk exposure.