  Multivariate classification methods using explanatory and predictive models
are necessary for characterizing subgroups of patients according to their risk
profiles. Popular methods include logistic regression and classification trees
with performances that vary according to the nature and the characteristics of
the dataset. In the context of imported malaria, we aimed at classifying
severity criteria based on a heterogeneous patient population. We investigated
these approaches by implementing two different strategies: L1 logistic
regression (L1LR) that models a single global solution and classification trees
that model multiple local solutions corresponding to discriminant subregions of
the feature space. For each strategy, we built a standard model, and a sparser
version of it. As an alternative to pruning, we explore a promising approach
that first constrains the tree model with an L1LR-based feature selection, an
approach we called L1LR-Tree. The objective is to decrease its vulnerability to
small data variations by removing variables corresponding to unstable local
phenomena. Our study is twofold: i) from a methodological perspective comparing
the performances and the stability of the three previous methods, i.e L1LR,
classification trees and L1LR-Tree, for the classification of severe forms of
imported malaria, and ii) from an applied perspective improving the actual
classification of severe forms of imported malaria by identifying more
personalized profiles predictive of several clinical criteria based on
variables dismissed for the clinical definition of the disease. The main
methodological results show that the combined method L1LR-Tree builds sparse
and stable models that significantly predicts the different severity criteria
and outperforms all the other methods in terms of accuracy.
