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

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 predic-tive 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 pre-dictive 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.
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https://hal.archives-ouvertes.fr/hal-01736935
Contributor : Bienvenue Kouwaye <>
Submitted on : Sunday, March 18, 2018 - 11:55:21 PM
Last modification on : Tuesday, May 26, 2020 - 3:42:46 AM
Long-term archiving on: : Tuesday, September 11, 2018 - 8:50:12 AM

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Bienvenue Kouwaye, Fabrice Rossi, Noël Fonton, André Garcia, Simplice Dossou-Gbété, et al.. Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm. PLoS ONE, Public Library of Science, 2017, 12 (10), ⟨10.1371/journal.pone.0187234⟩. ⟨hal-01736935⟩

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