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

• Autor(a) principal: Veiga, Rafael Valente
• Data de Publicação: 2021
• Outros Autores: Faccini, Lavinia Schuler, França, Giovanny Vinícius Araújo de, Andrade, Roberto Fernandes Silva, Teixeira, Maria Gloria, Costa, Larissa Catharina, Paixão, Enny Santos da, Costa, Maria da Conceição Nascimento, Barreto, Mauricio Lima, Oliveira, Juliane Fonseca de, Oliveira, Wanderson Kleber de, Cardim, Luciana Lobato, Rodrigues, Moreno Magalhães de Souza
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/267032
• topic: Zika virus; Microcefalia; Ciência da computação; Infecção viral; Aprendizado de máquina

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.