Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm
Publication Date
October 31, 2017
Authors
Bienvenue Kouwaye, Fabrice Rossi, Noël Fonton, André Garcia, et al
Volume
12
Issue
10
Pages
e0187234
DOI
https://dx.plos.org/10.1371/journal.pone.0187234
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0187234
Scopus
85032922866
Mendeley
http://www.mendeley.com/research/predicting-local-malaria-exposure-using-lassobased-twolevel-cross-validation-algorithm
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Mendeley | Further Information

{"title"=>"Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm", "type"=>"journal", "authors"=>[{"first_name"=>"Bienvenue", "last_name"=>"Kouwaye", "scopus_author_id"=>"54883875400"}, {"first_name"=>"Fabrice", "last_name"=>"Rossi", "scopus_author_id"=>"56347024200"}, {"first_name"=>"Noël", "last_name"=>"Fonton", "scopus_author_id"=>"54883875500"}, {"first_name"=>"André", "last_name"=>"Garcia", "scopus_author_id"=>"7404608228"}, {"first_name"=>"Simplice", "last_name"=>"Dossou-Gbété", "scopus_author_id"=>"12142102800"}, {"first_name"=>"Mahouton Norbert", "last_name"=>"Hounkonnou", "scopus_author_id"=>"7004597729"}, {"first_name"=>"Gilles", "last_name"=>"Cottrell", "scopus_author_id"=>"23110070000"}], "year"=>2017, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0187234", "sgr"=>"85032922866", "pui"=>"619011449", "scopus"=>"2-s2.0-85032922866", "issn"=>"19326203"}, "id"=>"086b7b9e-33a6-3f0d-abf8-8e0d9da33ca6", "abstract"=>"© 2017 Kouwaye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.", "link"=>"http://www.mendeley.com/research/predicting-local-malaria-exposure-using-lassobased-twolevel-cross-validation-algorithm", "reader_count"=>6, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Student > Master"=>1, "Professor"=>1, "Student > Ph. D. Student"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Student > Master"=>1, "Professor"=>1, "Student > Ph. D. Student"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Mathematics"=>1, "Decision Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Decision Sciences"=>{"Decision Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "group_count"=>0}

Scopus | Further Information

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