Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm

PloS One
Bienvenue KouwayeGilles Cottrell

Abstract

Recent studies have highlighted the importance of local environmental factors to determine the fine-scale heterogeneity of malaria transmission and exposure to the vector. In this work, we compare a classical GLM model with backward selection with different versions of an automatic LASSO-based algorithm with 2-level cross-validation aiming to build a predictive model of the space and time dependent individual exposure to the malaria vector, using entomological and environmental data from a cohort study in Benin. Although the GLM can outperform the LASSO model with appropriate engineering, the best model in terms of predictive power was found to be the LASSO-based model. Our approach can be adapted to different topics and may therefore be helpful to address prediction issues in other health sciences domains.

References

Feb 28, 1997·Statistics in Medicine·R Tibshirani
Jan 5, 2011·Malaria Journal·Georgia B DamienMarie-Claire Henry
Mar 10, 2015·Computational and Structural Biotechnology Journal·Konstantina KourouDimitrios I Fotiadis
Aug 22, 2016·BMC Bioinformatics·Songlu Li, Sejong Oh

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Software Mentioned

R
DCV
LOLO
LASSO

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