Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation

Abstract Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is neces...

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Autores principales:	Rafael V. Veiga, Lavinia Schuler-Faccini, Giovanny V. A. França, Roberto F. S. Andrade, Maria Glória Teixeira, Larissa C. Costa, Enny S. Paixão, Maria da Conceição N. Costa, Maurício L. Barreto, Juliane F. Oliveira, Wanderson K. Oliveira, Luciana L. Cardim, Moreno S. Rodrigues
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da4dfb4c20ba4f44a18f76a6fdf28499
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spelling	oai:doaj.org-article:da4dfb4c20ba4f44a18f76a6fdf284992021-12-02T17:04:06ZClassification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation10.1038/s41598-021-86361-52045-2322https://doaj.org/article/da4dfb4c20ba4f44a18f76a6fdf284992021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86361-5https://doaj.org/toc/2045-2322Abstract Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics.Rafael V. VeigaLavinia Schuler-FacciniGiovanny V. A. FrançaRoberto F. S. AndradeMaria Glória TeixeiraLarissa C. CostaEnny S. PaixãoMaria da Conceição N. CostaMaurício L. BarretoJuliane F. OliveiraWanderson K. OliveiraLuciana L. CardimMoreno S. RodriguesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rafael V. Veiga Lavinia Schuler-Faccini Giovanny V. A. França Roberto F. S. Andrade Maria Glória Teixeira Larissa C. Costa Enny S. Paixão Maria da Conceição N. Costa Maurício L. Barreto Juliane F. Oliveira Wanderson K. Oliveira Luciana L. Cardim Moreno S. Rodrigues Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
description	Abstract Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics.
format	article
author	Rafael V. Veiga Lavinia Schuler-Faccini Giovanny V. A. França Roberto F. S. Andrade Maria Glória Teixeira Larissa C. Costa Enny S. Paixão Maria da Conceição N. Costa Maurício L. Barreto Juliane F. Oliveira Wanderson K. Oliveira Luciana L. Cardim Moreno S. Rodrigues
author_facet	Rafael V. Veiga Lavinia Schuler-Faccini Giovanny V. A. França Roberto F. S. Andrade Maria Glória Teixeira Larissa C. Costa Enny S. Paixão Maria da Conceição N. Costa Maurício L. Barreto Juliane F. Oliveira Wanderson K. Oliveira Luciana L. Cardim Moreno S. Rodrigues
author_sort	Rafael V. Veiga
title	Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
title_short	Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
title_full	Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
title_fullStr	Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
title_full_unstemmed	Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
title_sort	classification algorithm for congenital zika syndrome: characterizations, diagnosis and validation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/da4dfb4c20ba4f44a18f76a6fdf28499
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Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation

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