A Regression Based Approach for Leishmaniasis Outbreak Detection

Descripción del Articulo

Leishmaniasis is part of a group of diseases called Neglected Tropical Diseases (NTDs) that affects poor and forgotten communities and reports more than 5,000 cases in regions like Brazil, Peru, and Colombia being categorized as endemic in these. In this study, we present a machine-learning model (R...

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Detalles Bibliográficos
Autores: Baptista, Ernie, Vigil, Franco, Ugarte, Willy
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/676005
Enlace del recurso:http://hdl.handle.net/10757/676005
Nivel de acceso:acceso embargado
Materia:Leishmaniasis
Machine Learning
NTDs
Outbreaks
Random Forest
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dc.title.es_PE.fl_str_mv A Regression Based Approach for Leishmaniasis Outbreak Detection
title A Regression Based Approach for Leishmaniasis Outbreak Detection
spellingShingle A Regression Based Approach for Leishmaniasis Outbreak Detection
Baptista, Ernie
Leishmaniasis
Machine Learning
NTDs
Outbreaks
Random Forest
title_short A Regression Based Approach for Leishmaniasis Outbreak Detection
title_full A Regression Based Approach for Leishmaniasis Outbreak Detection
title_fullStr A Regression Based Approach for Leishmaniasis Outbreak Detection
title_full_unstemmed A Regression Based Approach for Leishmaniasis Outbreak Detection
title_sort A Regression Based Approach for Leishmaniasis Outbreak Detection
author Baptista, Ernie
author_facet Baptista, Ernie
Vigil, Franco
Ugarte, Willy
author_role author
author2 Vigil, Franco
Ugarte, Willy
author2_role author
author
dc.contributor.author.fl_str_mv Baptista, Ernie
Vigil, Franco
Ugarte, Willy
dc.subject.es_PE.fl_str_mv Leishmaniasis
Machine Learning
NTDs
Outbreaks
Random Forest
topic Leishmaniasis
Machine Learning
NTDs
Outbreaks
Random Forest
description Leishmaniasis is part of a group of diseases called Neglected Tropical Diseases (NTDs) that affects poor and forgotten communities and reports more than 5,000 cases in regions like Brazil, Peru, and Colombia being categorized as endemic in these. In this study, we present a machine-learning model (Random Forest) to predict cases in the future and predict possible outbreaks using meteorological and epidemiological data of the province of la Convencion (Cusco - Peru). Understanding how climate variables affect leishmaniasis outbreaks is an important problem to help people to perform prevention systems. We used several techniques to obtain better metrics and improve our model performance such as synthetic data and hyperparameter optimization. Results showed two important climate factors to analyze and no outbreaks.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-10-05T10:52:17Z
dc.date.available.none.fl_str_mv 2024-10-05T10:52:17Z
dc.date.issued.fl_str_mv 2024-01-01
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.doi.none.fl_str_mv 10.5220/0012683900003699
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/676005
dc.identifier.eissn.none.fl_str_mv 21844984
dc.identifier.journal.es_PE.fl_str_mv International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings
dc.identifier.eid.none.fl_str_mv 2-s2.0-85193932782
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85193932782
identifier_str_mv 10.5220/0012683900003699
21844984
International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings
2-s2.0-85193932782
SCOPUS_ID:85193932782
url http://hdl.handle.net/10757/676005
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.es_PE.fl_str_mv application/html
dc.publisher.es_PE.fl_str_mv Science and Technology Publications, Lda
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
instacron_str UPC
institution UPC
reponame_str UPC-Institucional
collection UPC-Institucional
dc.source.journaltitle.none.fl_str_mv International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings
dc.source.beginpage.none.fl_str_mv 204
dc.source.endpage.none.fl_str_mv 211
bitstream.url.fl_str_mv https://repositorioacademico.upc.edu.pe/bitstream/10757/676005/1/license.txt
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
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repository.name.fl_str_mv Repositorio académico upc
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spelling 5765d6cee858adf096dfe9fa2ff0ec6e300629882bfb24753eb5cc30d32ee275a63300533fd7e68213307170565ef90452257a500Baptista, ErnieVigil, FrancoUgarte, Willy2024-10-05T10:52:17Z2024-10-05T10:52:17Z2024-01-0110.5220/0012683900003699http://hdl.handle.net/10757/67600521844984International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings2-s2.0-85193932782SCOPUS_ID:85193932782Leishmaniasis is part of a group of diseases called Neglected Tropical Diseases (NTDs) that affects poor and forgotten communities and reports more than 5,000 cases in regions like Brazil, Peru, and Colombia being categorized as endemic in these. In this study, we present a machine-learning model (Random Forest) to predict cases in the future and predict possible outbreaks using meteorological and epidemiological data of the province of la Convencion (Cusco - Peru). Understanding how climate variables affect leishmaniasis outbreaks is an important problem to help people to perform prevention systems. We used several techniques to obtain better metrics and improve our model performance such as synthetic data and hyperparameter optimization. Results showed two important climate factors to analyze and no outbreaks.application/htmlengScience and Technology Publications, Ldainfo:eu-repo/semantics/embargoedAccessLeishmaniasisMachine LearningNTDsOutbreaksRandom ForestA Regression Based Approach for Leishmaniasis Outbreak Detectioninfo:eu-repo/semantics/articleInternational Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings204211reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/676005/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/676005oai:repositorioacademico.upc.edu.pe:10757/6760052024-10-05 10:52:19.728Repositorio académico upcupc@openrepository.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A Regression Based Approach for Leishmaniasis Outbreak Detection
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